Positron emission tomography (PET) with ¹⁸F-fluorodeoxyglucose (FDG) has been regarded as the gold-standard technique to quantify brain energy metabolism. Despite standardized uptake values (SUV) reflecting regional FDG uptake, intrinsic networks derived from FDG-PET using independent-component analysis (ICA) could reflect long-distance metabolic brain connections. Based on this data-driven technique, the characteristics of metabolic resting-state networks (RSN) could be utilized to observe a cognitive decline in Alzheimer’s disease and amyotrophic lateral sclerosis (Pagani et al., 2016, 2017). Another promising aspect of FDG-PET is to couple glucose utilization with cerebral blood flow and oxygen consumption. Blood-oxygenation-level-dependent (BOLD) functional magnetic resonance imaging (fMRI) has been commonly used to non-invasively obtain oxygen metabolism and hemodynamic response related to brain activity (Bernier et al., 2017). The neurometabolic coupling between fMRI and FDG PET metrics has provided informative biomarkers for characterizing focal differences of energy demand in a healthy population and pathology processes such as alcohol exposure and disorders of consciousness (Soddu et al., 2016; Tomasi et al., 2017; Shokri-Kojori et al., 2019).

The integrated PET/MRI system could simultaneously obtain both fMRI and FDG PET images (Disselhorst et al., 2014; Chen et al., 2018). With the advantage of precisely matched structural localization and consistent physiological states, the combined system could provide a great opportunity for multiparametric analysis of interactions between brain metabolism and function (Heiss, 2009; Verger and Guedj, 2018). With the addition of FDG PET, fMRI showed potential for baseline shifts in quantifiable metabolism and neuronal signaling (Riedl et al., 2014, 2016). Also, with the fusion of fMRI, FDG PET could track dynamic task-related hemodynamic and metabolic interactions with higher temporal resolution (Jamadar et al., 2019). Recently, the integration of the two modalities has proved to be cost-effective for clinical research, especially in neurodegenerative disorders. Focal alterations between FDG uptake and fMRI metrics obtained by integrated PET/MRI showed potential to reveal signaling hierarchies in hippocampal–cortical circuits and default mode networks in patients with Alzheimer’s disease (Tahmasian et al., 2015, 2016; Marchitelli et al., 2018; Scherr et al., 2019; Yan et al., 2020). Altered bioenergetic coupling across the gray matter and its relationship with seizure outcomes were also reported in patients with medial temporal lobe epilepsy (Wang et al., 2020). Thus, simultaneous PET/fMRI has become a widely adopted approach for non-invasive brain metabolic research.

In order to shorten the time of scan, some clinical research using the integrated PET/MRI system prefer to run MRI sequences during the FDG uptake phase (the first 40 min after the radiotracer administration). However, the acoustic, thermal, and electromagnetic effect imposed by MRI on the human brain may result in physiological inference on quantitative FDG uptake curve. Although it has been assessed on phantoms that the stability of PET quantitation was not affected during simultaneous MRI scan, even when an aggressive sequence such as fast-spin echo MRI sequence was performed (Grant et al., 2016; Deller et al., 2018; Chen et al., 2021), the impact of MRI sequence pulsing was found in human studies to affect brain activation, including auditory, visual, and motor functional cortex (Di Salle et al., 2001; Tomasi et al., 2005; Zhang et al., 2005; Fuchino et al., 2006; Gaab et al., 2008; Andoh et al., 2017). A recent study has shown that the acoustic noise produced by fast switching gradients could cause a reproducible increase ranging from 3 to 9% in FDG uptake, which is limited to the primary auditory cortex (Chonde et al., 2013). Therefore, the timing of the fMRI scan is preferred to be run during the plateau phase (30–40 min after the radiotracer administration) of the FDG uptake curve in clinical routine. During this period, the most irreversible FDG distribution is considered to be finished with only a minimal amount of free FDG in the blood pool. However, few studies have proven that the impact induced by MRI scan during this “static” period could be limited to an acceptable range in human brain. Also, whether the possible effect could influence a specific metabolic parameter or neurometabolic coupling limited to the focal brain cortex, network level or whole-brain level, is largely unknown.

Therefore, the principal purpose of this work is to systematically evaluate the impact of simultaneous fMRI scan on the quantification of brain metabolism using an integrated PET/MRI system. We designed a randomized self-control study on healthy volunteers using clinically relevant imaging protocols. We will focus on the additional effect induced by fMRI sequence pulsing on voxel-wise energy consumption, spatial distribution of metabolic networks as well as the correlation between regional glucose metabolism and functional metrics within and across different resting-state functional networks.

To the best of our knowledge, this work is the first study to systematically assess the impact of simultaneous fMRI scan on FDG-PET in human brain with an integrated PET/MRI system. Our protocol is a self-control study design following the recommended clinical routine, where MRI sequences were performed 30–40 min after the injection of ¹⁸F-FDG. It is commonly considered that the FDG-PET data acquired during this plateau phase of the uptake curve mainly represents the neuronal activity that occurred during the preceding early uptake phase. Thus, SUVr for the plateau phase is supposed to be steady even when fMRI scan is synchronously performed. Our results found no difference in either mean SUVr or voxel-wise SUVr compared between MRI-on and MRI-off periods, which supported this inference. However, an obvious increase of SUVr slope was detected in the MRI-on period across the whole brain, especially in gray matter, located in sensorimotor, attention, control, default, and auditory networks. This network-wise metabolic change reflects a short-term FDG uptake elevation due to the fMRI scan. We assume that this phenomenon was mainly in favor of a neuronal origin whose activation mostly locate in gray matter regions and may also be due to other unspecific physiological inferences, such as the temperature-dependent acceleration of metabolic rates. However, with only a minor amount of free FDG available in the blood pool, fMRI scan in the steady state could only produce a limited, short-term effect on the trend of FDG elevation, which could be presented by the elevation of SUVr slope but does not affect the calculation of an accumulative SUVr value to statistical significance.

In the network-wise comparison, when treating fMRI scan as a stimulation task, the affected components were located in the default, auditory, visual, and language networks, which were commonly regarded as “higher-order” cognitive networks. A previous study reported that 13 meaningful RSNs could be detected from FDG-PET data acquired 10–30 min post-injection. Among them, seven networks could be detected by both modalities, including default mode, left central executive, primary and secondary visual, sensorimotor, cerebellar, and auditory networks (Savio et al., 2017). In our study, the “activated” networks induced by fMRI scan fundamentally located in these “dually” detected RSNs, which could be explained by changes of either cerebral blood flow or activity-dependent glucose consumption. As our data was acquired 30 min post-injection, the contribution of a blood flow signal change caused by the instant injection of FDG could be negligible. In this way, these “activations” observed in our ICA results should be regarded as comparable elevated glucose consumption rather than increased cerebral blood flow or oxygen consumption.

In previous studies, the possible mechanism underlying neurometabolic coupling was explained by temporally synchronized cerebral blood flow and energy utilization based on the theory that resting-state glucose and oxygen metabolism were closely linked (Wang et al., 2021). Strong coupling was found in default and visual networks, while a weak correlation was found in limbic and somatomotor network (Aiello et al., 2015). The highest correlation between rCMRGlu and fMRI metrics was achieved in ReHo, which were both detected by using integrated PET/MRI or separated PET and MRI devices (Aiello et al., 2015; Bernier et al., 2017; Jiao et al., 2019). The other metrics, such as ALFF or DC, showed a lower association with CMRGlu, maybe strongly affected by the venous vasculature or other non-neuronal factors on signal amplitude (Bernier et al., 2017). These differences could also be explained by the different physiological phenomena probed by each metric. ALFF contrast is only due to a single voxel signal. ReHo could be considered as a measurement of short-range functional connectivity (FC) affected by the neighboring 27 voxels, while DC measures distant voxels weighted by long-range FC in the whole brain.

Our study reports a synthesis effect of MRI scan on quantitative PET in an integrated system. Among all possible factors, acoustic MRI noise resulting from echo-planar imaging should be regarded as the main concern. This sequence is normally accompanied by a gradient-shifting noise with a sound level greater than 100 dB (Zhang et al., 2005). Studies have focused on measuring how background acoustic noise influenced the hemodynamic responses in the auditory cortex and made efforts to spoil the interference (Di Salle et al., 2001). Reduced activation in the visual cortex was also reported, which may relate to attention modulation due to auditory–visual cross-modal neural interaction (Zhang et al., 2005). Increased activation of working memory network (Tomasi et al., 2005) and suppressed activation in the default-mode network and sensorimotor cortex (Fuchino et al., 2006; Gaab et al., 2008) were, respectively, discussed under the presence of BOLD-related noise. “Quieter” fMRI acquisition methods, such as sparse temporal sampling or interleaved silent steady state, could be applied to a less noisy background environment for BOLD-fMRI scan (Andoh et al., 2017). In addition, MRI-induced RF power deposition and the resulting effects on temperature-dependent metabolic rates could also influence FDG uptake, with maximum relative increases of 26% for uptake models based on metabolism (Carluccio et al., 2017). We speculate that these abovementioned factors synergistically influenced brain metabolism during the static phase of FDG uptake in our study.

This work was subject to several limitations. First, we adopted a blood-free approach to estimate the relative quantification of CMRGlu, which is more tolerable for a universal clinical routine. However, for a more precise design, absolute quantification of CMRGlu could be calculated by infusion of ¹⁸F-FDG and venous blood sampling (Hahn et al., 2016). Second, methodologically, ICA and seed-based functional connectivity (sbFC) are two main approaches for the statistical mapping of RSNs derived from FDG-PET. It has been discussed that the choice of ICA or sbFC could influence the detectability of RSNs, especially when the sample size is limited (Trotta et al., 2018). Future studies could retest and verify our results by different data analysis methods based on a larger dataset.

The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.

The studies involving human participants were reviewed and approved by the Medical Research Ethics Committee of Xuanwu Hospital, Capital Medical University (Beijing, China). The patients/participants provided their written informed consent to participate in this study.

YS, ZW, QX, and YZ contributed to the conception and design of the study. YS, ZW, SS, QX, and QG performed the formal analysis. YS and ZW wrote the first draft of the manuscript. BC and HY contributed to project administration and visualization. JL, YZ, and MZ contributed to supervision and validation. JL contributed to funding acquisition. All authors contributed to manuscript revision and read and approved the submitted version.

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

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