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Although the brain cannot store glucose, it relies on glucose as its primary energy source. The basal metabolic demand of the brain is estimated to be 30% of whole-brain glucose use, providing the fuel required for functions such as resting state connections. Seventy percent of brain glucose use is for spontaneous functional processes, including the production of action potentials, postsynaptic potentials, and the maintenance of ion gradients. The level of cerebral glucose metabolism is considered a reliable measure of neuronal activity. [F-18] fluorodeoxyglucose ([18F]-FDG or FDG) positron emission tomography (PET) is the most commonly used neuroimaging technique for directly evaluating brain glucose metabolism in vivo. In recent systematic reviews and meta-analyses of FDG-PET studies in ageing, we found older adults have lower frontal and temporal cerebral metabolic rates of glucose (CMRGLC) than younger adults (Deery et al., 2022). This pattern was found when effect size meta-analyses were performed at the lobular levels and activation likelihood estimates meta-analyses were undertaken at a regional level. The age effects were reduced after adjustments were made for grey matter and/or partial volume effects. The action of insulin in the brain has received attention recently because significant disturbances are not only observed in obesity and diabetes, but also in ageing and dementia. Although there is a large body of epidemiological evidence linking metabolic health, including insulin sensitivity, to ageing and disease, the relationship to CMRGLC in ageing has not been studied. The current study will assess the relationships between peripheral insulin sensitivity, as measured by Homeostatic Model Assessment for Insulin Resistance (HOMA-IR), age, cortical thickness and grey matter volume, CMRGLC at the level of large-scale networks, and cognitive function.

Classic psychedelic-induced ego dissolution involves a shift in the sense of self and a blurring of the boundary between the self and the world. A similar phenomenon is identified in psychopathology and is associated with the balance of anticorrelated activity between the default mode network, which directs attention inward, and the salience network, which recruits the dorsal attention network to direct attention outward.To test whether changes in anticorrelated networks underlie the peak effects of lysergic acid diethylamide (LSD), we applied dynamic causal modeling to infer effective connectivity of resting-state functional magnetic resonance imaging scans from a study of 25 healthy adults who were administered 100 μg of LSD or placebo.We found that inhibitory effective connectivity from the salience network to the default mode network became excitatory, and inhibitory effective connectivity from the default mode network to the dorsal attention network decreased under the peak effect of LSD.The effective connectivity changes we identified may reflect diminution of the functional anticorrelation between resting-state networks that may be a key neural mechanism of LSD and underlie ego dissolution. Our findings suggest that changes to the sense of self and subject-object boundaries across different states of consciousness may depend upon the organized balance of effective connectivity of resting-state networks.

Although the brain cannot store glucose, it relies on glucose as its primary energy source. The basal metabolic demand of the brain is estimated to be 30% of whole-brain glucose use, providing the fuel required for functions such as resting state connections. Seventy percent of brain glucose use is for spontaneous functional processes, including the production of action potentials, postsynaptic potentials, and the maintenance of ion gradients. The level of cerebral glucose metabolism is considered a reliable measure of neuronal activity. [F-18] fluorodeoxyglucose ([18F]-FDG or FDG) positron emission tomography (PET) is the most commonly used neuroimaging technique for directly evaluating brain glucose metabolism in vivo. In recent systematic reviews and meta-analyses of FDG-PET studies in ageing, we found older adults have lower frontal and temporal cerebral metabolic rates of glucose (CMRGLC) than younger adults (Deery et al., 2022). This pattern was found when effect size meta-analyses were performed at the lobular levels and activation likelihood estimates meta-analyses were undertaken at a regional level. The age effects were reduced after adjustments were made for grey matter and/or partial volume effects. The action of insulin in the brain has received attention recently because significant disturbances are not only observed in obesity and diabetes, but also in ageing and dementia. Although there is a large body of epidemiological evidence linking metabolic health, including insulin sensitivity, to ageing and disease, the relationship to CMRGLC in ageing has not been studied. The current study will assess the relationships between peripheral insulin sensitivity, as measured by Homeostatic Model Assessment for Insulin Resistance (HOMA-IR), age, CMRGLC at the regional level using Statistical Parameter Mapping, and cognitive function.

Simultaneous FDG-PET/fMRI ([18F]-fluorodeoxyglucose positron emission tomography functional magnetic resonance imaging) provides the capacity to image two sources of energetic dynamics in the brain – glucose metabolism and haemodynamic response. Functional fMRI connectivity has been enormously useful for characterising interactions between distributed brain networks in humans. Metabolic connectivity based on static FDG-PET has been proposed as a biomarker for neurological disease; but static FDG-PET cannot be used to estimate subjectlevel measures of connectivity, only across-subject covariance. Here, we applied high-temporal resolution constant infusion fPET to measure subject-level metabolic connectivity simultaneously with fMRI connectivity. fPET metabolic connectivity was characterised by fronto-parietal connectivity within and between hemispheres. fPET metabolic connectivity showed moderate similarity with fMRI primarily in superior cortex and frontoparietal regions. Significantly, fPET metabolic connectivity showed little similarity with static FDG-PET metabolic covariance, indicating that metabolic brain connectivity is a non-ergodic process whereby individual brain connectivity cannot be inferred from group level metabolic covariance. Our results highlight the complementary strengths of fPET and fMRI in measuring the intrinsic connectivity of the brain, and open up the opportunity for novel fundamental studies of human brain connectivity as well as multi-modality biomarkers of neurological diseases.

Studies of task-evoked brain activity are the cornerstone of cognitive neuroscience, and unravel the spatial and temporal brain dynamics of cognition in health and disease. Blood oxygenation level dependent functional magnetic resonance imaging (BOLD-fMRI) is one of the most common methods of studying brain function in humans. BOLD-fMRI indirectly infers neuronal activity from regional changes in blood oxygenation and is not a quantitative metric of brain function. Regional variation in glucose metabolism, measured using [18-F] fluorodeoxyglucose positron emission tomography (FDG-PET), provides a more direct and interpretable measure of neuronal activity. However, while the temporal resolution of BOLD-fMRI is in the order of seconds, standard FDG-PET protocols provide a static snapshot of glucose metabolism. Here, we develop a novel experimental design for measurement of task-evoked changes in regional blood oxygenation and glucose metabolism with high temporal resolution. Over a 90-min simultaneous BOLD-fMRI/FDG-PET scan, [18F] FDG was constantly infused to 10 healthy volunteers, who viewed a flickering checkerboard presented in a hierarchical block design. Dynamic task-related changes in blood oxygenation and glucose metabolism were examined with temporal resolution of 2.5sec and 1-min, respectively. Task-related, temporally coherent brain networks of haemodynamic and metabolic connectivity were maximally related in the visual cortex, as expected. Results demonstrate that the hierarchical block design, together with the infusion FDG-PET technique, enabled both modalities to track task-related neural responses with high temporal resolution. The simultaneous MR-PET approach has the potential to provide unique insights into the dynamic haemodynamic and metabolic interactions that underlie cognition in health and disease.

Head motion is a major source of image artefacts in neuroimaging studies and can lead to degradation of the quantitative accuracy of reconstructed PET images. Simultaneous Magnetic Resonance-Positron Emission Tomography (MR-PET) makes it possible to estimate head motion information from high-resolution MR images and then correct motion artefacts in PET images. In this paper, we introduce a fully automated PET motion correction method, MR-guided MAF, based on the co-registration of multi-contrast MR images. The performance of the MR-guided MAF method was evaluated using MR-PET data acquired from a cohort of ten healthy participants who received a slow infusion of fluorodeoxyglucose ([18-F]FDG). Compared with conventional methods, MR guided PET image reconstruction can reduce head motion introduced artefacts and improve the image sharpness and quantitative accuracy of PET images acquired using simultaneous MR-PET scanners. The fully automated motion estimation method has been implemented as a publicly available web-service.

Purpose: The suppression of motion artefacts from MR images is a challenging task. The purpose of this paper is to develop a standalone novel technique to suppress motion artefacts from MR images using a data-driven deep learning approach. Methods: A deep learning convolutional neural network (CNN) was developed to remove motion artefacts in brain MR images. A CNN was trained on simulated motion corrupted images to identify and suppress artefacts due to the motion. The network was an encoder-decoder CNN architecture where the encoder decomposed the motion corrupted images into a set of feature maps. The feature maps were then combined by the decoder network to generate a motion-corrected image. The network was tested on an unseen simulated dataset and an experimental, motion corrupted in vivo brain dataset. Results: The trained network was able to suppress the motion artefacts in the simulated motion corrupted images, and the mean percentage error in the motion corrected images was 2.69 % with a standard deviation of 0.95 %. The network was able to effectively suppress the motion artefacts from the experimental dataset, demonstrating the generalisation capability of the trained network. Conclusion: A novel and generic motion correction technique has been developed that can suppress motion artefacts from motion corrupted MR images. The proposed technique is a standalone post-processing method that does not interfere with data acquisition or reconstruction parameters, thus making it suitable for a multitude of MR sequences.