Temporally coupled FDG-fPET and BOLD-fMRI dynamics across task and naturalistic arousal
Poster No:
2409
Submission Type:
Abstract Submission
Authors:
Sean Coursey1,2, Shirley Feng1, Jingyuan Chen1,3
Institutions:
1Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, 2College of Science, Northeastern University, Boston, MA, 3Department of Radiology, Harvard Medical School, Boston, MA
First Author:
Sean Coursey
Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital|College of Science, Northeastern University
Charlestown, MA|Boston, MA
Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital|College of Science, Northeastern University
Charlestown, MA|Boston, MA
Co-Author(s):
Shirley Feng
Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital
Charlestown, MA
Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital
Charlestown, MA
Jingyuan Chen
Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital|Department of Radiology, Harvard Medical School
Charlestown, MA|Boston, MA
Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital|Department of Radiology, Harvard Medical School
Charlestown, MA|Boston, MA
Introduction:
The recent introduction of continuous-infusion FDG-PET (FDG-fPET) and modern PET-MRI scanners permits examining concurrent dynamics of glucose metabolism and blood-oxygenation-level-dependent (BOLD) signals in the human brain[1-3]. Despite fPET's higher temporal resolution than previous PET methods[4], most fPET-fMRI studies to date have centered on static spatial correlations[5-7]. Leveraging fPET's temporal resolution, our study introduces an analytical framework connecting the evolution of FDG-fPET signals with concurrent BOLD-fMRI data. Tested on visual-stimulus and naturalistic-arousal datasets, this methodology proved effective in linking glucose uptake and BOLD dynamics.
Methods:
We first assessed the temporal coupling of BOLD-fMRI and FDG-fPET measures by analyzing a public visual-stimulation dataset[8]. The Monash vis-fPET-fMRI dataset comprises 70-minute simultaneous FDG-fPET and BOLD-fMRI scans of 10 healthy young adults (fMRI: voxel size = 3×3×3 mm3, TR = 2.45 s; fPET: reconstructed nominal voxel size = 1.39×1.39×2 mm3, temporal resolution = 1 min). Participants were shown a flickering checkerboard in an embedded block task design (Fig. 1a). We hypothesize that the power of the fMRI signal at the stimulus frequency is indicative of neural activity, so integrating this power over time will correspond to the concurrent FDG accumulation due to metabolic demand. To test this, we computed the average fMRI signal from the primary visual cortex (V1) across subjects, applied a Hilbert transform to determine the BOLD signal power over time, and integrated this power to use as a predictor variable for the FDG-fPET data (Fig. 1a). We removed long-term trends from both the predictor and the PET data using a third-order polynomial, focusing on the dynamic changes.
To test our model for a naturalistic paradigm, we acquired and analyzed fPET-fMRI data from 23 healthy adults who were instructed to close their eyes and relax throughout a 75-120 min scan. Arousal states were inferred from simultaneous EEG recordings (18 subjects) or behavioral measures (5 subjects). All scans were performed on a 3T Siemens MR scanner with a BrainPET insert (fMRI: voxel size = 3×3×3 mm3, TR = 2/2.4 s; fPET: reconstructed nominal voxel size = 2.5×2.5×2.5 mm3, temporal resolution = 30 s). FDG was administered as a bolus plus continuous infusion. Following a similar framework as the visual dataset, we tested how much variance the integrated power of the global fMRI signal could explain in subjects' fPET time activity curves (TACs).
To test our model for a naturalistic paradigm, we acquired and analyzed fPET-fMRI data from 23 healthy adults who were instructed to close their eyes and relax throughout a 75-120 min scan. Arousal states were inferred from simultaneous EEG recordings (18 subjects) or behavioral measures (5 subjects). All scans were performed on a 3T Siemens MR scanner with a BrainPET insert (fMRI: voxel size = 3×3×3 mm3, TR = 2/2.4 s; fPET: reconstructed nominal voxel size = 2.5×2.5×2.5 mm3, temporal resolution = 30 s). FDG was administered as a bolus plus continuous infusion. Following a similar framework as the visual dataset, we tested how much variance the integrated power of the global fMRI signal could explain in subjects' fPET time activity curves (TACs).
Results:
Regression showed that our integrated V1 BOLD power predictor aligns with V1 FDG accumulation (Fig. 1a). Moreover, the significant correlation between the BOLD signal and FDG accumulation is specifically strong in the visual cortex (Fig. 1b). We also noticed metabolic activations in the frontal cortex, possibly owing to task-locked attention changes.
Our tri-modal framework successfully tracked sleep-wake dynamics in both glucose uptake and hemodynamic changes (Fig. 2a). We identified strong coupling between the global BOLD and FDG time-courses (Fig. 2b). The metabolic regressor modeled by the global fMRI signal explained significant variance of fPET TACs in extensive cortical regions (Fig. 2c). These observations supported the applicability of the PET-MRI integration framework in naturalistic paradigms.
Our tri-modal framework successfully tracked sleep-wake dynamics in both glucose uptake and hemodynamic changes (Fig. 2a). We identified strong coupling between the global BOLD and FDG time-courses (Fig. 2b). The metabolic regressor modeled by the global fMRI signal explained significant variance of fPET TACs in extensive cortical regions (Fig. 2c). These observations supported the applicability of the PET-MRI integration framework in naturalistic paradigms.
Conclusions:
We created a framework that temporally links metabolic and hemodynamic signals. Functional MRI signals predicted concurrent FDG-fPET dynamics driven by visual stimulation and naturalistic arousal. Our results substantiate hemodynamic-metabolic temporal coupling, demonstrating the value of FDG-fPET in conjunction with BOLD-fMRI for characterizing time-resolved co-evolution of neurometabolic and neurovascular activity. Consequently, this study advances neuroimaging's exploration of hemodynamic-metabolic interdependence in the human brain.
Modeling and Analysis Methods:
Methods Development
PET Modeling and Analysis
Novel Imaging Acquisition Methods:
Multi-Modal Imaging 1
Perception, Attention and Motor Behavior:
Sleep and Wakefulness
Physiology, Metabolism and Neurotransmission :
Cerebral Metabolism and Hemodynamics 2
Keywords:
Acquisition
Data analysis
Design and Analysis
Electroencephaolography (EEG)
FUNCTIONAL MRI
Modeling
Positron Emission Tomography (PET)
Sleep
Statistical Methods
Other - Multimodal imaging, PET/MRI
1|2Indicates the priority used for review
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[3] Jamadar, S.D. (2019), 'Simultaneous task-based BOLD-fMRI and [18-F] FDG functional PET for measurement of neuronal metabolism in the human visual cortex', Neuroimage, 189, pp.258-266.
[4] Rischka, L. (2018), 'Reduced task durations in functional PET imaging with [18F] FDG approaching that of functional MRI', Neuroimage, 181, pp.323-330.
[5] Stiernman, L.J. (2021), 'Dissociations between glucose metabolism and blood oxygenation in the human default mode network revealed by simultaneous PET-fMRI', Proceedings of the National Academy of Sciences, 118(27), p.e2021913118.
[6] Hahn, A. (2020), 'Reconfiguration of functional brain networks and metabolic cost converge during task performance', elife, 9, p.e52443.
[7] Jamadar, S.D. (2021), 'Metabolic and hemodynamic resting-state connectivity of the human brain: a high-temporal resolution simultaneous BOLD-fMRI and FDG-fPET multimodality study', Cerebral Cortex, 31(6), pp.2855-2867.
[8] Jamadar, S.D. (2021), 'Task-evoked simultaneous FDG-PET and fMRI data for measurement of neural metabolism in the human visual cortex', Scientific Data, 8(1), p.267.