Unveiling the biological substrates of glucose metabolic covariance in human brain
Presented During:
Saturday, June 28, 2025: 11:30 AM - 12:45 PM
Brisbane Convention & Exhibition Centre
Room:
Great Hall
Poster No:
1258
Submission Type:
Abstract Submission
Authors:
mengyuan Liu1, Min Wang1, zhoukang Wu1, Liangjiecheng Huang2, Aobo Chen2, Yaotian Gao2, Joseph I Tracy3, Xiaosong He2
Institutions:
1Department of Psychology, University of Science and Technology of China, Hefei, Anhui, 2Department of Psychology, University of Science and Technology of China, Hefei, P.R. China., HeFei, Ahui, 3Department of Neurology, Thomas Jefferson University, Philadelphia, PA
First Author:
Co-Author(s):
Liangjiecheng Huang
Department of Psychology, University of Science and Technology of China, Hefei, P.R. China.
HeFei, Ahui
Department of Psychology, University of Science and Technology of China, Hefei, P.R. China.
HeFei, Ahui
Aobo Chen
Department of Psychology, University of Science and Technology of China, Hefei, P.R. China.
HeFei, Ahui
Department of Psychology, University of Science and Technology of China, Hefei, P.R. China.
HeFei, Ahui
Yaotian Gao
Department of Psychology, University of Science and Technology of China, Hefei, P.R. China.
HeFei, Ahui
Department of Psychology, University of Science and Technology of China, Hefei, P.R. China.
HeFei, Ahui
Xiaosong He
Department of Psychology, University of Science and Technology of China, Hefei, P.R. China.
HeFei, Ahui
Department of Psychology, University of Science and Technology of China, Hefei, P.R. China.
HeFei, Ahui
Introduction:
Glucose metabolism is essential for providing the energetic supply that supports the neural dynamics vital for brain function. Abnormal glucose metabolism, such as hypo- and hypermetabolism, is frequently reported in patients with neurological diseases, including Alzheimer's disease and epilepsy. It has become clear that regional metabolic changes are not isolated incidents; rather, local inhibitory circuitry or diaschisis, due to connections from pathological regions, may also affect local metabolism. These remote impacts, which can be characterized by metabolic covariance networks (MCNs), have been found to be indicative of brain pathologies [1]. However, the biological substrates of such covariance-specifically, the basis on which this covariance emerges-remain to be elucidated. Based on multimodal imaging data from a group of temporal lobe epilepsy (TLE) patients, we explore the fundamental determinants of MCN, including intrinsic functional connectivity (FC), white matter pathways (structural connectivity, SC), morphometric similarity (structural covariance network, SCN), geometric proximity (Euclidean distance, ED), as well as neurochemical and genetic underpinnings.
Methods:
Multimodal imaging data, including 18F-FDG PET, HARDI, rs-fMRI, and T1, were collected from 45 patients with TLE for presurgical evaluations. After preprocessing following standard pipelines, the images were normalized to MNI space. A modified version of the Lausanne Atlas [2] was used to parcel the brain into 122 regions of interest (ROIs), from which MCN, FC, SC, SCN, ED, receptor similarity networks (REC) [3], and gene co-expression networks (GENE) [4] were constructed (Fig. 1A). All metrics were scaled to [-1, 1] or [0, 1], and multiple linear regressions were applied to evaluate the contributions of these biological factors in shaping the MCN. The whole brain network was further divided into 8 intrinsic connectivity networks (ICNs), and multiple linear regressions were conducted on each ICN to identify the biological determinants of intra-network metabolic covariance.
Results:
Pearson correlation analyses confirmed that, consistent with previous reports [5, 6], MCN is positively associated with SC and FC, highlighting the contributions of white matter pathways and intrinsic functional synchrony in facilitating metabolic covariances. Additionally, we found that regions with higher morphometric similarity and shorter geometric distances exhibited similar metabolic patterns. Furthermore, the co-expression of neurotransmitter/transporter receptors and genes among regions was also positively correlated with metabolic co-fluctuations, emphasizing the role of neurochemical and genetic influences in shaping this energetic landscape (Fig. 1B).
Subsequently, we constructed a multiple linear regression model with all six matrices as independent variables and the MCN as the dependent variable. This model explained a substantial portion of the variance in MCN (F(7,7253) = 1623, P < 0.001, adjusted R² = 0.573), with each factor making a significant contribution (Fig. 2AB). The largest contributions came from intrinsic functional connectivity, geometric proximity, and receptor similarity. When examining their contributions to predicting MCN at the level of individual ICNs, we observed heterogeneous patterns. While all 8 models were effective in predicting metabolic covariance within each ICN (Fs ≥ 9.88, P < 0.001, adjusted R² ≥ 0.548), not all factors contributed significantly to every model. Interestingly, no single factor contributed to all models, not even FC. (Fig. 2C)
Subsequently, we constructed a multiple linear regression model with all six matrices as independent variables and the MCN as the dependent variable. This model explained a substantial portion of the variance in MCN (F(7,7253) = 1623, P < 0.001, adjusted R² = 0.573), with each factor making a significant contribution (Fig. 2AB). The largest contributions came from intrinsic functional connectivity, geometric proximity, and receptor similarity. When examining their contributions to predicting MCN at the level of individual ICNs, we observed heterogeneous patterns. While all 8 models were effective in predicting metabolic covariance within each ICN (Fs ≥ 9.88, P < 0.001, adjusted R² ≥ 0.548), not all factors contributed significantly to every model. Interestingly, no single factor contributed to all models, not even FC. (Fig. 2C)
Conclusions:
We demonstrated that at the global level, MCN is shaped by multiple biological factors, including genetics, chemoarchitecture, geometry, morphometry, white matter pathways, and intrinsic brain activity. Within each ICN, however, metabolic co-fluctuations were driven by different biological factors, suggesting distinct mechanisms underlying metabolic patterns across brain networks.
Modeling and Analysis Methods:
Connectivity (eg. functional, effective, structural) 1
PET Modeling and Analysis 2
Univariate Modeling
Physiology, Metabolism and Neurotransmission:
Cerebral Metabolism and Hemodynamics
Keywords:
Epilepsy
MRI
Positron Emission Tomography (PET)
Structures
WHITE MATTER IMAGING - DTI, HARDI, DSI, ETC
Other - Multiple linear regressions
1|2Indicates the priority used for review
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[2] He, Xiaosong, et al. "Uncovering the biological basis of control energy: Structural and metabolic correlates of energy inefficiency in temporal lobe epilepsy." Science Advances 8.45 (2022): eabn2293.
[3] Hansen, Justine Y., et al. "Mapping neurotransmitter systems to the structural and functional organization of the human neocortex." Nature neuroscience 25.11 (2022): 1569-1581.
[4] Hawrylycz, Michael J., et al. "An anatomically comprehensive atlas of the adult human brain transcriptome." Nature 489.7416 (2012): 391-399.
[5] Yakushev, Igor, et al. "Mapping covariance in brain FDG uptake to structural connectivity." European journal of nuclear medicine and molecular imaging (2022): 1-10.
[6] Di, Xin, and Bharat B. Biswal, and Alzheimer's Disease Neuroimaging Initiative. "Metabolic brain covariant networks as revealed by FDG-PET with reference to resting-state fMRI networks." Brain connectivity 2.5 (2012): 275-283.