Coupling between brain degree centrality and glucose metabolism across tasks and disease conditions
Poster No:
2101
Submission Type:
Abstract Submission
Authors:
Shirley Feng1, Sean Coursey1, Nicole Zürcher2, Hsiao-Ying Wey2, Jonathan Polimeni2, Marjorie Villien1, Anisha Bhanot1, Jacob Hooker2, Jingyuan Chen2
Institutions:
1Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Boston, MA, 2Massachusetts General Hospital and Harvard Medical School, Boston, MA
First Author:
Shirley Feng
Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital
Boston, MA
Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital
Boston, MA
Co-Author(s):
Sean Coursey
Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital
Boston, MA
Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital
Boston, MA
Marjorie Villien
Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital
Boston, MA
Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital
Boston, MA
Anisha Bhanot
Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital
Boston, MA
Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital
Boston, MA
Introduction:
Graph theory-based network analysis has provided profound insights into the topological organization of the fMRI-based brain functional connectome and its alterations across broad tasks and disease conditions [1,2]. Surprisingly, despite the widespread adoption of many complex network metrics, such as degree centrality (DC), in fMRI research, their neurobiological underpinnings remain poorly understood. Recent studies have reported a positive association between regional DC and glucose uptake during the resting state [3,4], yet it is unclear whether this extends to task and disease conditions. Specifically, when a brain region exhibits elevated DC (i.e., becoming more "hub"-like), does its energetic demand also increase? To test this hypothesis, we combined empirical experiments and a literature meta-analysis to examine and compare changes in regional DC and metabolism across sensory/cognitive tasks and mental disorders.
Methods:
We first assessed how task conditions mediate the relationship between regional DC and glucose metabolism, using simultaneous functional PET (fPET)-FDG/fMRI data acquired under four different conditions: visual, motor, working memory, and sleep. Functional PET (fPET)-FDG is a new technique that enables us to track task-evoked changes in glucose metabolism within a single scan [5]. The visual task dataset was publicly available [6], and the remaining three fPET/fMRI datasets were collected locally at Massachusetts General Hospital. All analyses were performed at the region of interest (ROI) level, using a multi-resolution functional atlas "7 network-300 ROI version" [7]. Task-induced metabolic (de)activations were characterized using a general linear model (GLM), following established fPET-FDG studies [5,8]. DC was calculated from thresholded and binarized functional connectivity matrices of parcellated fMRI data.
Next, to investigate how disease influences the relationship between regional DC and glucose metabolism, we conducted a meta-analysis of resting-state fMRI studies employing graph theory and bolus-injection FDG-PET studies across four psychiatric/neurological disorders: Alzheimer's Disease (AD), Parkinson's Disease (PD), depression, and schizophrenia. For each disorder, we calculated the probability of each brain region showing statistically significant alterations in DC or glucose metabolism from existing literature. Probability of each brain region having congruent/incongruent polarity of change in DC and CMRglu across four disorders were mapped in AAL atlas with 90 regions [9].
Next, to investigate how disease influences the relationship between regional DC and glucose metabolism, we conducted a meta-analysis of resting-state fMRI studies employing graph theory and bolus-injection FDG-PET studies across four psychiatric/neurological disorders: Alzheimer's Disease (AD), Parkinson's Disease (PD), depression, and schizophrenia. For each disorder, we calculated the probability of each brain region showing statistically significant alterations in DC or glucose metabolism from existing literature. Probability of each brain region having congruent/incongruent polarity of change in DC and CMRglu across four disorders were mapped in AAL atlas with 90 regions [9].
Results:
Figure 1 summarizes task/sleep-induced network metabolic/DC changes, and their consistency of polarity. The level of overall coupling between glucose metabolism and DC varied across tasks/sleep: changes in glucose uptake from rest to visual and motor tasks and from wake to sleep were negatively correlated with DC change, while changes in glucose uptake from rest to working memory task correlated positively with DC change. Cortical regions with the same polarity of change in glucose metabolism and DC are shown in pink/green, while regions with opposite polarity of change are depicted in yellow/navy.
Figure 2A summarizes the differences in resting-state glucose metabolism and DC in AD, PD, depression, and schizophrenia compared to healthy controls. Similar to task results, only a subset of regions exhibited congruent polarity between diseased-related changes in DC and metabolic measures. Across brain disorders, the polarity of changes in glucose metabolism and DC was most consistent in the orbital-frontal and visual cortices, and least consistent in the precuneus (Fig. 2B).
Figure 2A summarizes the differences in resting-state glucose metabolism and DC in AD, PD, depression, and schizophrenia compared to healthy controls. Similar to task results, only a subset of regions exhibited congruent polarity between diseased-related changes in DC and metabolic measures. Across brain disorders, the polarity of changes in glucose metabolism and DC was most consistent in the orbital-frontal and visual cortices, and least consistent in the precuneus (Fig. 2B).
Conclusions:
Altogether, our results suggest that the coupling between fMRI-based functional connectivity DC and glucose metabolism varies significantly across cortical regions and task/disease conditions. These observations highlight a complex relationship between topological network metrics derived from complex network theory and brain energetics.
Modeling and Analysis Methods:
fMRI Connectivity and Network Modeling 2
Novel Imaging Acquisition Methods:
Multi-Modal Imaging
Physiology, Metabolism and Neurotransmission:
Cerebral Metabolism and Hemodynamics 1
Keywords:
Computational Neuroscience
FUNCTIONAL MRI
Meta- Analysis
Other - Graph Theory; Metabolism; PET-MRI; Connectivity
1|2Indicates the priority used for review
Abstract Information
By submitting your proposal, you grant permission for the Organization for Human Brain Mapping (OHBM) to distribute your work in any format, including video, audio print and electronic text through OHBM OnDemand, social media channels, the OHBM website, or other electronic publications and media.
The Open Science Special Interest Group (OSSIG) is introducing a reproducibility challenge for OHBM 2025. This new initiative aims to enhance the reproducibility of scientific results and foster collaborations between labs. Teams will consist of a “source” party and a “reproducing” party, and will be evaluated on the success of their replication, the openness of the source work, and additional deliverables. Click here for more information. Propose your OHBM abstract(s) as source work for future OHBM meetings by selecting one of the following options:
Please indicate below if your study was a "resting state" or "task-activation” study.
Healthy subjects only or patients (note that patient studies may also involve healthy subjects):
Was this research conducted in the United States?
Are you Internal Review Board (IRB) certified? Please note: Failure to have IRB, if applicable will lead to automatic rejection of abstract.
Were any human subjects research approved by the relevant Institutional Review Board or ethics panel? NOTE: Any human subjects studies without IRB approval will be automatically rejected.
Were any animal research approved by the relevant IACUC or other animal research panel? NOTE: Any animal studies without IACUC approval will be automatically rejected.
Please indicate which methods were used in your research:
For human MRI, what field strength scanner do you use?
Which processing packages did you use for your study?
Provide references using APA citation style.
[2] Bullmore, E., & Sporns, O. (2009). Complex brain networks: graph theoretical analysis of structural and functional systems. Nature Reviews. Neuroscience, 10(3), 186–198.
[3] Tomasi, D., Wang, G.-J., & Volkow, N. D. (2013). Energetic cost of brain functional connectivity. Proceedings of the National Academy of Sciences, 110(33), 13642–13647.
[4] Shokri-Kojori, E., Tomasi, D., Alipanahi, B., Wiers, C.E., Wang, G.J. and Volkow, N.D., (2019). Correspondence between cerebral glucose metabolism and BOLD reveals relative power and cost in human brain. Nature Communications, 10(1), p.690.
[5] Villien, M., Wey, H.Y., Mandeville, J.B., Catana, C., Polimeni, J.R., Sander, C.Y., Zürcher, N.R., Chonde, D.B., Fowler, J.S., Rosen, B.R. and Hooker, J.M., (2014). Dynamic functional imaging of brain glucose utilization using fPET-FDG. Neuroimage, 100, pp.192-199.
[6] Jamadar, S. D., Ward, P. G. D., Close, T. G., Fornito, A., Premaratne, M., O’Brien, K., Stäb, D., Chen, Z., Shah, N. J., & Egan, G. F. (2020). Simultaneous BOLD-fMRI and constant infusion FDG-PET data of the resting human brain. Scientific Data, 7(1).
[7] Schaefer, A., Kong, R., Gordon, E. M., Laumann, T. O., Zuo, X., Holmes, A. J., Eickhoff, S. B., & Yeo, B. T. T. (2017). Local-Global Parcellation of the Human Cerebral Cortex from Intrinsic Functional Connectivity MRI. Cerebral Cortex, 28(9), 3095–3114.
[8] Hahn, A., Gryglewski, G., Nics, L., Hienert, M., Rischka, L., Vraka, C., Sigurdardottir, H., Vanicek, T., James, G.M., Seiger, R. and Kautzky, A., (2016). Quantification of task-specific glucose metabolism with constant infusion of 18F-FDG. Journal of Nuclear Medicine, 57(12), pp.1933-1940.
[9] Tzourio-Mazoyer, N., Landeau, B., Papathanassiou, D., Crivello, F., Etard, O., Delcroix, N., Mazoyer, B., & Joliot, M. (2002). Automated Anatomical labeling of activations in SPM using a macroscopic anatomical parcellation of the MNI MRI Single-Subject Brain. NeuroImage, 15(1), 273–289.