Analysis of continuous infusion functional PET (fPET) in the human brain

Metabolic connectivity has greater predictive utility for age and cognition than functional connectivity

Recently developed high temporal resolution functional (18F)-fluorodeoxyglucose positron emission tomography (fPET) offers promise as a method for indexing the dynamic metabolic state of the brain in vivo by directly measuring a time series of metabolism at the post-synaptic neuron. This is distinct from functional magnetic resonance imaging (fMRI) that reflects a combination of metabolic, haemodynamic and vascular components of neuronal activity. The value of using fPET to understand healthy brain ageing and cognition over fMRI is currently unclear. Here, we use simultaneous fPET/fMRI to compare metabolic and functional connectivity and test their predictive ability for ageing and cognition. Whole-brain fPET connectomes showed moderate topological similarities to fMRI connectomes in a cross-sectional comparison of 40 younger (mean age 27.9 years; range 20-42) and 46 older (mean 75.8; 60-89) adults. There were more age-related within- and between-network connectivity and graph metric differences in fPET than fMRI. fPET was also associated with performance in more cognitive domains than fMRI. These results suggest that ageing is associated with a reconfiguration of metabolic connectivity that differs from haemodynamic alterations. We conclude that metabolic connectivity has greater predictive utility for age and cognition than functional connectivity and that measuring glucodynamic changes has promise as a biomarker for age-related cognitive decline.

Neuromark PET: A multivariate method for estimating whole brain fMRI guided intrinsic networks and connectomes from fMRI and PET data

Positron emission tomography (PET) and magnetic resonance imaging (MRI) are both widely used neuroimaging techniques to study brain functional and molecular connectivity. Although whole brain resting functional MRI (fMRI) connectomes (a matrix describing the inter-regional connectivity patterns) are widely used, the integration or association of whole brain molecular connectomes with PET data are rarely done. This likely stems from the fact that PET data is typically analyzed by using a region of interest approach, while whole brain spatial networks and their connectivity (covariation) receive much less attention. As a result, to date, there have been little focus on directly comparing whole brain PET and fMRI connectomes. In this study, we present a method that uses spatially constrained independent component analysis (scICA) (utilizing fMRI components as spatial priors) to estimate corresponding (Amyloid) PET and fMRI connectomes and examine the relationship between them using datasets that include individuals with mild cognitive impairment (MCI). Our results demonstrate highly modularized PET connectome patterns that complement those identified from resting fMRI. In particular, fMRI showed strong intra-domain connectivity with interdomain anticorrelation in sensorimotor and visual domains as well as default mode network. PET amyloid data showed similar strong intra-domain effects, but showed much higher correlations within cognitive control and default mode domains, as well as anticorrelation between cerebellum and other domains. The estimated fMRI informed PET networks have similar, but not identical, network spatial patterns to the resting fMRI networks, with the fMRI informed PET networks being slightly smoother and, in some cases, showing variations in subnodes. To further compare the two modalities, we also analyzed the differences between individuals with MCI receiving medication versus a placebo. Results show both common and modality specific treatment effects on fMRI and PET connectomes. From our fMRI analysis, we observed higher connectivity differences in various regions, such as the connection between the thalamus and middle occipital gyrus, as well as the insula and right middle occipital gyrus. Meanwhile, the PET analysis revealed increased activation between the anterior cingulate cortex and the left inferior parietal lobe, along with other regions, in individuals who received medication versus placebo. In sum, our novel approach identifies corresponding whole-brain fMRI informed PET and fMRI networks and connectomes. While we observed common patterns of network connectivity, our analysis of the MCI treatment and placebo groups revealed that each modality captures modality and group specific information about brain networks, highlighting differences between the two groups in both network expression and network connectivity.

Functional PET/MRI reveals active inhibition of neuronal activity during optogenetic activation of the nigrostriatal pathway

The dopaminergic system is a central component of the brain's neurobiological framework, governing motor control and reward responses and playing an essential role in various brain disorders. Within this complex network, the nigrostriatal pathway represents a critical circuit for dopamine neurotransmission from the substantia nigra to the striatum. However, stand-alone functional magnetic resonance imaging is unable to study the intricate interplay between brain activation and its molecular underpinnings. In our study, the use of a functional [fluorine-18]2-fluor-2-deoxy-d-glucose positron emission tomography approach, simultaneously with blood oxygen level-dependent functional magnetic resonance imaging, provided an important insight that demonstrates an active suppression of the nigrostriatal activity during optogenetic stimulation. This result increases our understanding of the molecular mechanisms of brain function and provides an important perspective on how dopamine influences hemodynamic responses in the brain.

A novel method for PET connectomics guided by fibre-tracking MRI: Application to Alzheimer's disease

This study introduces a novel brain connectome matrix, track-weighted PET connectivity (twPC) matrix, which combines positron emission tomography (PET) and diffusion magnetic resonance imaging data to compute a PET-weighted connectome at the individual subject level. The new method is applied to characterise connectivity changes in the Alzheimer's disease (AD) continuum. The proposed twPC samples PET tracer uptake guided by the underlying white matter fibre-tracking streamline point-to-point connectivity calculated from diffusion MRI (dMRI). Using tau-PET, dMRI and T1-weighted MRI from the Alzheimer's Disease Neuroimaging Initiative database, structural connectivity (SC) and twPC matrices were computed and analysed using the network-based statistic (NBS) technique to examine topological alterations in early mild cognitive impairment (MCI), late MCI and AD participants. Correlation analysis was also performed to explore the coupling between SC and twPC. The NBS analysis revealed progressive topological alterations in both SC and twPC as cognitive decline progressed along the continuum. Compared to healthy controls, networks with decreased SC were identified in late MCI and AD, and networks with increased twPC were identified in early MCI, late MCI and AD. The altered network topologies were mostly different between twPC and SC, although with several common edges largely involving the bilateral hippocampus, fusiform gyrus and entorhinal cortex. Negative correlations were observed between twPC and SC across all subject groups, although displaying an overall reduction in the strength of anti-correlation with disease progression. twPC provides a new means for analysing subject-specific PET and MRI-derived information within a hybrid connectome using established network analysis methods, providing valuable insights into the relationship between structural connections and molecular distributions. PRACTITIONER POINTS: New method is proposed to compute patient-specific PET connectome guided by MRI fibre-tracking. Track-weighted PET connectivity (twPC) matrix allows to leverage PET and structural connectivity information. twPC was applied to dementia, to characterise the PET nework abnormalities in Alzheimer's disease and mild cognitive impairment.

Functional FDG-PET: Measurement of Task Related Neural Activity in Humans-A Compartment Model Approach and Comparison to fMRI

Neuroimaging holds an essential position in global healthcare, as brain-related disorders are a substantial and growing burden. Non-degenerative disorders such as stress, depression and anxiety share common function related traits of diffuse and fluctuating changes, such as change in brain-based functions of mood, behavior and cognitive abilities, where underlying physiological mechanism remain unresolved. In this study we developed a novel application for studying intra-subject task-activated brain function by the quantitative physiological measurement of the change in glucose metabolism in a single scan setup. Data were acquired on a PET/MR-scanner. We implemented a functional [18F]-FDG PET-scan with double boli-tracer administration and finger-tapping activation, as proof-of-concept, in five healthy participants. The [18F]-FDG data were analyzed using a two-tissue compartment double boli kinetic model with an image-derived input function. For stand-alone visual reference, blood oxygenation level dependent (BOLD) functional MRI (fMRI) was acquired in the same session and analyzed separately. We were able to measure the cerebral glucose metabolic rate during baseline as well as activation. Results showed increased glucose metabolic rate during activation by 36.3-87.9% mean 62.0%, locally in the peak seed region of M1 in the brain, on an intra-subject level, as well as very good spatial accuracy on group level, and localization compared to the BOLD fMRI result at subject and group level. Our novel method successfully determined the relative increase in the cerebral metabolic rate of glucose on a voxel level with good visual association to fMRI at the subject-level, holding promise for future individual clinical application. This approach will be easily adapted in future clinical perspectives and pharmacological interventions studies.

Brain connectomics: time for a molecular imaging perspective?

In the past two decades brain connectomics has evolved into a major concept in neuroscience. However, the current perspective on brain connectivity and how it underpins brain function relies mainly on the hemodynamic signal of functional magnetic resonance imaging (MRI). Molecular imaging provides unique information inaccessible to MRI-based and electrophysiological techniques. Thus, positron emission tomography (PET) has been successfully applied to measure neural activity, neurotransmission, and proteinopathies in normal and pathological cognition. Here, we position molecular imaging within the brain connectivity framework from the perspective of timeliness, validity, reproducibility, and resolution. We encourage the neuroscientific community to take an integrative approach whereby MRI-based, electrophysiological techniques, and molecular imaging contribute to our understanding of the brain connectome.

Functional imaging of the exposed brain

When the brain is exposed, such as after a craniotomy in neurosurgical procedures, we are provided with the unique opportunity for real-time imaging of brain functionality. Real-time functional maps of the exposed brain are vital to ensuring safe and effective navigation during these neurosurgical procedures. However, current neurosurgical practice has yet to fully harness this potential as it pre-dominantly relies on inherently limited techniques such as electrical stimulation to provide functional feedback to guide surgical decision-making. A wealth of especially experimental imaging techniques show unique potential to improve intra-operative decision-making and neurosurgical safety, and as an added bonus, improve our fundamental neuroscientific understanding of human brain function. In this review we compare and contrast close to twenty candidate imaging techniques based on their underlying biological substrate, technical characteristics and ability to meet clinical constraints such as compatibility with surgical workflow. Our review gives insight into the interplay between technical parameters such sampling method, data rate and a technique's real-time imaging potential in the operating room. By the end of the review, the reader will understand why new, real-time volumetric imaging techniques such as functional Ultrasound (fUS) and functional Photoacoustic Computed Tomography (fPACT) hold great clinical potential for procedures in especially highly eloquent areas, despite the higher data rates involved. Finally, we will highlight the neuroscientific perspective on the exposed brain. While different neurosurgical procedures ask for different functional maps to navigate surgical territories, neuroscience potentially benefits from all these maps. In the surgical context we can uniquely combine healthy volunteer studies, lesion studies and even reversible lesion studies in in the same individual. Ultimately, individual cases will build a greater understanding of human brain function in general, which in turn will improve neurosurgeons' future navigational efforts.

The older adult brain is less modular, more integrated, and less efficient at rest: A systematic review of large-scale resting-state functional brain networks in aging

The literature on large-scale resting-state functional brain networks across the adult lifespan was systematically reviewed. Studies published between 1986 and July 2021 were retrieved from PubMed. After reviewing 2938 records, 144 studies were included. Results on 11 network measures were summarized and assessed for certainty of the evidence using a modified GRADE method. The evidence provides high certainty that older adults display reduced within-network and increased between-network functional connectivity. Older adults also show lower segregation, modularity, efficiency and hub function, and decreased lateralization and a posterior to anterior shift at rest. Higher-order functional networks reliably showed age differences, whereas primary sensory and motor networks showed more variable results. The inflection point for network changes is often the third or fourth decade of life. Age effects were found with moderate certainty for within- and between-network altered patterns and speed of dynamic connectivity. Research on within-subject bold variability and connectivity using glucose uptake provides low certainty of age differences but warrants further study. Taken together, these age-related changes may contribute to the cognitive decline often seen in older adults.

Lower brain glucose metabolism in normal ageing is predominantly frontal and temporal: A systematic review and pooled effect size and activation likelihood estimates meta-analyses

This review provides a qualitative and quantitative analysis of cerebral glucose metabolism in ageing. We undertook a systematic literature review followed by pooled effect size and activation likelihood estimates (ALE) meta-analyses. Studies were retrieved from PubMed following the PRISMA guidelines. After reviewing 635 records, 21 studies with 22 independent samples (n = 911 participants) were included in the pooled effect size analyses. Eight studies with eleven separate samples (n = 713 participants) were included in the ALE analyses. Pooled effect sizes showed significantly lower cerebral metabolic rates of glucose for older versus younger adults for the whole brain, as well as for the frontal, temporal, parietal, and occipital lobes. Among the sub-cortical structures, the caudate showed a lower metabolic rate among older adults. In sub-group analyses controlling for changes in brain volume or partial volume effects, the lower glucose metabolism among older adults in the frontal lobe remained significant, whereas confidence intervals crossed zero for the other lobes and structures. The ALE identified nine clusters of lower glucose metabolism among older adults, ranging from 200 to 2640 mm³ . The two largest clusters were in the left and right inferior frontal and superior temporal gyri and the insula. Clusters were also found in the inferior temporal junction, the anterior cingulate and caudate. Taken together, the results are consistent with research showing less efficient glucose metabolism in the ageing brain. The findings are discussed in the context of theories of cognitive ageing and are compared to those found in neurodegenerative disease.

Mechanisms supporting the social regulation of neural threat responding with marital partners: A test of the opioid hypothesis

Positive social contact predicts better health, but the mechanisms for this association remain debated. One way to explore this link is through the social regulation of emotion, particularly anticipatory anxiety. Previous research finds less neural threat response during partner handholding than when people are alone or stranger handholding. Various mechanistic accounts have been forwarded, including the hypothesis that this effect is mediated by endogenous opioid activity. This experiment critically tested the opioid hypothesis in 60 married participants and their partners. The study used a naltrexone opioid blockade in a double-blind placebo control with functional magnetic resonance imaging to determine whether endogenous opioids were necessary for handholding effects. Regulatory effects of supportive handholding manifested in threat network regions during opioid blockade, but not with placebo. Despite a surprising lack of effect in the placebo group, the overall study findings provide initial evidence that endogenous opioids may not be necessary for the social regulation of neural threat responding.

Assessing different approaches to estimate single-subject metabolic connectivity from dynamic [18F]fluorodeoxyglucose Positron Emission Tomography data

Metabolic connectivity is conventionally calculated in terms of correlation of static positron emission tomography (PET) measurements across subjects. There is increasing interest in deriving metabolic connectivity at the single-subject level from dynamic PET data, in a similar way to functional magnetic resonance imaging. However, the strong multicollinearity among region-wise PET time-activity curves (TACs), their non-Gaussian distribution, and the choice of the best strategy for TAC standardization before metabolic connectivity estimation, are non-trivial methodological issues to be tackled.In this work we test four different approaches to estimate sparse inverse covariance matrices, as well as three similarity-based methods to derive adjacency matrices. These approaches, combined with three different TAC standardization strategies, are employed to quantify metabolic connectivity from dynamic [¹⁸F]fluorodeoxyglucose ([¹⁸F]FDG) PET data in four healthy subjects.

Task-evoked simultaneous FDG-PET and fMRI data for measurement of neural metabolism in the human visual cortex

Understanding how the living human brain functions requires sophisticated in vivo neuroimaging technologies to characterise the complexity of neuroanatomy, neural function, and brain metabolism. Fluorodeoxyglucose positron emission tomography (FDG-PET) studies of human brain function have historically been limited in their capacity to measure dynamic neural activity. Simultaneous [18 F]-FDG-PET and functional magnetic resonance imaging (fMRI) with FDG infusion protocols enable examination of dynamic changes in cerebral glucose metabolism simultaneously with dynamic changes in blood oxygenation. The Monash vis-fPET-fMRI dataset is a simultaneously acquired FDG-fPET/BOLD-fMRI dataset acquired from n = 10 healthy adults (18-49 yrs) whilst they viewed a flickering checkerboard task. The dataset contains both raw (unprocessed) images and source data organized according to the BIDS specification. The source data includes PET listmode, normalization, sinogram and physiology data. Here, the technical feasibility of using opensource frameworks to reconstruct the PET listmode data is demonstrated. The dataset has significant re-use value for the development of new processing pipelines, signal optimisation methods, and to formulate new hypotheses concerning the relationship between neuronal glucose uptake and cerebral haemodynamics.

Towards lower-dose PET using physics-based uncertainty-aware multimodal learning with robustness to out-of-distribution data

Radiation exposure in positron emission tomography (PET) imaging limits its usage in the studies of radiation-sensitive populations, e.g., pregnant women, children, and adults that require longitudinal imaging. Reducing the PET radiotracer dose or acquisition time reduces photon counts, which can deteriorate image quality. Recent deep-neural-network (DNN) based methods for image-to-image translation enable the mapping of low-quality PET images (acquired using substantially reduced dose), coupled with the associated magnetic resonance imaging (MRI) images, to high-quality PET images. However, such DNN methods focus on applications involving test data that match the statistical characteristics of the training data very closely and give little attention to evaluating the performance of these DNNs on new out-of-distribution (OOD) acquisitions. We propose a novel DNN formulation that models the (i) underlying sinogram-based physics of the PET imaging system and (ii) the uncertainty in the DNN output through the per-voxel heteroscedasticity of the residuals between the predicted and the high-quality reference images. Our sinogram-based uncertainty-aware DNN framework, namely, suDNN, estimates a standard-dose PET image using multimodal input in the form of (i) a low-dose/low-count PET image and (ii) the corresponding multi-contrast MRI images, leading to improved robustness of suDNN to OOD acquisitions. Results on in vivo simultaneous PET-MRI, and various forms of OOD data in PET-MRI, show the benefits of suDNN over the current state of the art, quantitatively and qualitatively.

Incorporation of anatomical MRI knowledge for enhanced mapping of brain metabolism using functional PET

Functional positron emission tomography (fPET) imaging using continuous infusion of [18F]-fluorodeoxyglucose (FDG) is a novel neuroimaging technique to track dynamic glucose utilization in the brain. In comparison to conventional static or dynamic bolus PET, fPET maintains a sustained supply of glucose in the blood plasma which improves sensitivity to measure dynamic glucose changes in the brain, and enables mapping of dynamic brain activity in task-based and resting-state fPET studies. However, there is a trade-off between temporal resolution and spatial noise due to the low concentration of FDG and the limited sensitivity of multi-ring PET scanners. Images from fPET studies suffer from partial volume errors and residual scatter noise that may cause the cerebral metabolic functional maps to be biased. Gaussian smoothing filters used to denoise the fPET images are suboptimal, as they introduce additional partial volume errors. In this work, a post-processing framework based on a magnetic resonance (MR) Bowsher-like prior was used to improve the spatial and temporal signal to noise characteristics of the fPET images. The performance of the MR guided method was compared with conventional denosing methods using both simulated and in vivo task fPET datasets. The results demonstrate that the MR-guided fPET framework denoises the fPET images and improves the partial volume correction, consequently enhancing the sensitivity to identify brain activation, and improving the anatomical accuracy for mapping changes of brain metabolism in response to a visual stimulation task. The framework extends the use of functional PET to investigate the dynamics of brain metabolic responses for faster presentation of brain activation tasks, and for applications in low dose PET imaging.

Estimation of simultaneous BOLD and dynamic FDG metabolic brain activations using a multimodality concatenated ICA (mcICA) method

Simultaneous magnetic resonance and positron emission tomography provides an opportunity to measure brain haemodynamics and metabolism in a single scan session, and to identify brain activations from multimodal measurements in response to external stimulation. However, there are few analysis methods available for jointly analysing the simultaneously acquired blood-oxygen-level dependant functional MRI (fMRI) and 18-F-fluorodeoxyglucose functional PET (fPET) datasets. In this work, we propose a new multimodality concatenated ICA (mcICA) method to identify joint fMRI-fPET brain activations in response to a visual stimulation task. The mcICA method produces a fused map from the multimodal datasets with equal contributions of information from both modalities, measured by entropy. We validated the method in silico, and applied it to an in vivo visual stimulation experiment. The mcICA method estimated the activated brain regions in the visual cortex modulated by both BOLD and FDG signals. The mcICA provides a fully data-driven analysis approach to analyse cerebral haemodynamic response and glucose uptake signals arising from exogenously induced neuronal activity.

Simultaneous BOLD-fMRI and constant infusion FDG-PET data of the resting human brain

Simultaneous [18 F]-fluorodeoxyglucose positron emission tomography and functional magnetic resonance imaging (FDG-PET/fMRI) provides the capability to image two sources of energetic dynamics in the brain - cerebral glucose uptake and the cerebrovascular haemodynamic response. Resting-state fMRI connectivity has been enormously useful for characterising interactions between distributed brain regions in humans. Metabolic connectivity has recently emerged as a complementary measure to investigate brain network dynamics. Functional PET (fPET) is a new approach for measuring FDG uptake with high temporal resolution and has recently shown promise for assessing the dynamics of neural metabolism. Simultaneous fMRI/fPET is a relatively new hybrid imaging modality, with only a few biomedical imaging research facilities able to acquire FDG PET and BOLD fMRI data simultaneously. We present data for n = 27 healthy young adults (18-20 yrs) who underwent a 95-min simultaneous fMRI/fPET scan while resting with their eyes open. This dataset provides significant re-use value to understand the neural dynamics of glucose metabolism and the haemodynamic response, the synchrony, and interaction between these measures, and the development of new single- and multi-modality image preparation and analysis procedures.