Human Cerebral Cortex Organization Estimated by Functional PET-FDG “Metabolic Connectivity”
Poster No:
1496
Submission Type:
Abstract Submission
Authors:
Penghui Du1,2, Sean Coursey2,3, Ting Xu4, Hsiao-Ying Wey2,5, Jonathan Polimeni2,5,6, Quanying Liu1, Jingyuan Chen2,5
Institutions:
1Department of Biomedical Engineering, Southern University of Science and Technology, Shenzhen, Guangdong, China, 2Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA, 3College of Science, Northeastern University, Boston, MA, USA, 4Child Mind Institute, New York, NY, USA, 5Department of Radiology, Harvard Medical School, Boston, MA, USA, 6Harvard-MIT Division of Health Sciences and Technology, Massachusetts Institute of Technology, Cambridge, MA, USA
First Author:
Penghui Du
Department of Biomedical Engineering, Southern University of Science and Technology|Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital
Shenzhen, Guangdong, China|Charlestown, MA, USA
Department of Biomedical Engineering, Southern University of Science and Technology|Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital
Shenzhen, Guangdong, China|Charlestown, MA, USA
Co-Author(s):
Sean Coursey
Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital|College of Science, Northeastern University
Charlestown, MA, USA|Boston, MA, USA
Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital|College of Science, Northeastern University
Charlestown, MA, USA|Boston, MA, USA
Hsiao-Ying Wey, Assistant Professor
Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital|Department of Radiology, Harvard Medical School
Charlestown, MA, USA|Boston, MA, USA
Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital|Department of Radiology, Harvard Medical School
Charlestown, MA, USA|Boston, MA, USA
Jonathan Polimeni
Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital|Department of Radiology, Harvard Medical School|Harvard-MIT Division of Health Sciences and Technology, Massachusetts Institute of Technology
Charlestown, MA, USA|Boston, MA, USA|Cambridge, MA, USA
Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital|Department of Radiology, Harvard Medical School|Harvard-MIT Division of Health Sciences and Technology, Massachusetts Institute of Technology
Charlestown, MA, USA|Boston, MA, USA|Cambridge, MA, USA
Quanying Liu
Department of Biomedical Engineering, Southern University of Science and Technology
Shenzhen, Guangdong, China
Department of Biomedical Engineering, Southern University of Science and Technology
Shenzhen, Guangdong, China
Jingyuan Chen
Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital|Department of Radiology, Harvard Medical School
Charlestown, MA, USA|Boston, MA, USA
Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital|Department of Radiology, Harvard Medical School
Charlestown, MA, USA|Boston, MA, USA
Introduction:
The recent development of high-temporal resolution functional PET (fPET) introduces an emerging focus on "metabolic connectivity (MC)"1-3, providing a complementary perspective to the hemodynamic-based "functional connectivity (FC)" assessed by fMRI4-6. In this study, we applied a connectivity gradient-based analytical scheme on a resting-state simultaneous fPET-fMRI dataset, aiming to characterize the detailed cortical organization of fPET-derived MC and understand how it differs from the fMRI-derived functional network structures.
Methods:
The publicly available Monash rsPET-MR dataset7 was utilized for this study. A group of 26 healthy young adults each underwent a 95-minute simultaneous BOLD-fMRI (TR=2450 ms, 3×3x3 mm3 voxels) and constant-infusion FDG-PET scan (2.09x2.09x2.03 mm3 nominal voxels, 16 s per frame) while resting with their eyes open.
As summarized in Figure 1a, we first estimated four types of connectivity metrics: a) FC, temporal synchrony of instantaneous fMRI fluctuations; b) MC, temporal synchrony of instantaneous fPET fluctuations; c) metabolic covariance (MCov), across-subject covariance of the static PET-FDG measures1; and d) ALFF connectivity (ALFF-C), temporal synchrony of fMRI amplitude of low-frequency fluctuations (ALFF), using a time-windowed measure (width=34.3s, step=9.8s), motivated by established link between ALFF and glucose metabolism8.
After obtaining these four connectivity metrics, we quantified region-specific connectivity gradients following an analytical pipeline in previous study5, with larger gradients suggesting more rapidly changing connectivity patterns. Then a watershed algorithm was applied to identify potential boundaries between different brain regions. Finally, we derived multiple parcels from the boundary maps, and grouped them into separate networks using the Louvain community detection algorithm9.
As summarized in Figure 1a, we first estimated four types of connectivity metrics: a) FC, temporal synchrony of instantaneous fMRI fluctuations; b) MC, temporal synchrony of instantaneous fPET fluctuations; c) metabolic covariance (MCov), across-subject covariance of the static PET-FDG measures1; and d) ALFF connectivity (ALFF-C), temporal synchrony of fMRI amplitude of low-frequency fluctuations (ALFF), using a time-windowed measure (width=34.3s, step=9.8s), motivated by established link between ALFF and glucose metabolism8.
After obtaining these four connectivity metrics, we quantified region-specific connectivity gradients following an analytical pipeline in previous study5, with larger gradients suggesting more rapidly changing connectivity patterns. Then a watershed algorithm was applied to identify potential boundaries between different brain regions. Finally, we derived multiple parcels from the boundary maps, and grouped them into separate networks using the Louvain community detection algorithm9.
Results:
As illustrated in Fig. 1b, MC exhibited modest consistency of local gradients and boundaries with other metrics. At the network level (parcellation), we identified a prominent fronto-parietal component and an inferior temporal-occipital component in MC that deviated from the fMRI-derived FC and ALFF-C networks. Similar networks were also observed in MCov despite the dissociation of MC and Mcov in the connectivity profile.
We then applied high-pass and low-pass filtering to the fPET data with a cutoff at 1/300 Hz (5 minutes) (Fig. 2). While both contributed to the local features, the global organization of MC was primarily driven by low-frequency components. Although high-frequency components also exhibited connectivity patterns and networks, they were noisy and resembled patterns derived from random noise.
Surprisingly, we also noticed considerable across-subject consistency of the fPET time-activity curves (TACs). As shown in Fig. 2, both local and global features of group-averaged TACs to some extent resembled those derived from the individual data (Fig 2b). Synchronized fPET TACs across subjects may potentially stem from similar scanning experience coupled to experimental design, or from misspecification of baseline fPET TACs that have specific spatial signatures10.
We then applied high-pass and low-pass filtering to the fPET data with a cutoff at 1/300 Hz (5 minutes) (Fig. 2). While both contributed to the local features, the global organization of MC was primarily driven by low-frequency components. Although high-frequency components also exhibited connectivity patterns and networks, they were noisy and resembled patterns derived from random noise.
Surprisingly, we also noticed considerable across-subject consistency of the fPET time-activity curves (TACs). As shown in Fig. 2, both local and global features of group-averaged TACs to some extent resembled those derived from the individual data (Fig 2b). Synchronized fPET TACs across subjects may potentially stem from similar scanning experience coupled to experimental design, or from misspecification of baseline fPET TACs that have specific spatial signatures10.
Conclusions:
In this study, we explored cortical organization using fPET-based MC. We found that while MC shares some local gradient and parcellation similarities with FC, the overall MC networks deviated from conventional FC-based networks. Our results also suggested that the metabolic networks were dominated by low-frequency components (≥5min) and were partially driven by surprisingly synchronized fPET dynamics across subjects. We corroborate previous MC findings that fPET sheds complementary insights into the cerebral functional architecture; additionally, we highlight the complexity of interpreting the fPET-based MC and a need for examining the specific signal mechanisms in future studies.
Modeling and Analysis Methods:
Connectivity (eg. functional, effective, structural) 1
Physiology, Metabolism and Neurotransmission :
Cerebral Metabolism and Hemodynamics 2
Keywords:
Data analysis
FUNCTIONAL MRI
Positron Emission Tomography (PET)
Other - Metabolic Connectivity; PET-MRI
1|2Indicates the priority used for review
Provide references using author date format
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