Protein synthesis is associated with high-speed dynamics and broad-band stability of functional hubs in the brain

The brain's "dark energy" puzzle upgraded: [18F]FDG uptake, delivery and phosphorylation, and their coupling with resting-state brain activity

The brain's resting-state energy consumption is expected to be driven by spontaneous activity. We previously used 50 resting-state fMRI (rs-fMRI) features to predict [18F]FDG SUVR as a proxy of glucose metabolism. Here, we expanded on our effort by estimating [18F]FDG kinetic parameters Ki (irreversible uptake), K1 (delivery), k3 (phosphorylation) in a large healthy control group (n = 47). Describing the parameters' spatial distribution at high resolution (216 regions), we showed that K1 is the least redundant (strong posteromedial pattern), and Ki and k3 have relevant differences (occipital cortices, cerebellum, thalamus). Using multilevel modeling, we investigated how much spatial variance of [18F]FDG parameters could be explained by a combination of a) rs-fMRI variables, b) cerebral blood flow (CBF) and metabolic rate of oxygen (CMRO2) from 15O PET. Rs-fMRI-only models explained part of the individual variance in Ki (35%), K1 (14%), k3 (21%), while combining rs-fMRI and CMRO2 led to satisfactory description of Ki (46%) especially. Ki was sensitive to both local rs-fMRI variables (ReHo) and CMRO2, k3 to ReHo, K1 to CMRO2. This work represents a comprehensive assessment of the complex underpinnings of brain glucose consumption, and highlights links between 1) glucose phosphorylation and local brain activity, 2) glucose delivery and oxygen consumption.

The brain's "dark energy" puzzle upgraded: [18F]FDG uptake, delivery and phosphorylation, and their coupling with resting-state brain activity

The brain's resting-state energy consumption is expected to be mainly driven by spontaneous activity. In our previous work, we extracted a wide range of features from resting-state fMRI (rs-fMRI), and used them to predict [18F]FDG PET SUVR as a proxy of glucose metabolism. Here, we expanded upon our previous effort by estimating [18F]FDG kinetic parameters according to Sokoloff's model, i.e.,K i (irreversible uptake rate),K 1 (delivery),k 3 (phosphorylation), in a large healthy control group. The parameters' spatial distribution was described at a high spatial resolution. We showed that whileK 1 is the least redundant, there are relevant differences betweenK i andk 3 (occipital cortices, cerebellum and thalamus). Using multilevel modeling, we investigated how much of the regional variability of [18F]FDG parameters could be explained by a combination of rs-fMRI variables only, or with the addition of cerebral blood flow (CBF) and metabolic rate of oxygen (CMRO2), estimated from 15O PET data. We found that combining rs-fMRI and CMRO2 led to satisfactory prediction of individualK i variance (45%). Although more difficult to describe,K i andk 3 were both most sensitive to local rs-fMRI variables, whileK 1 was sensitive to CMRO2. This work represents the most comprehensive assessment to date of the complex functional and metabolic underpinnings of brain glucose consumption.

The brain's "dark energy" puzzle: How strongly is glucose metabolism linked to resting-state brain activity?

Brain glucose metabolism, which can be investigated at the macroscale level with [18F]FDG PET, displays significant regional variability for reasons that remain unclear. Some of the functional drivers behind this heterogeneity may be captured by resting-state functional magnetic resonance imaging (rs-fMRI). However, the full extent to which an fMRI-based description of the brain's spontaneous activity can describe local metabolism is unknown. Here, using two multimodal datasets of healthy participants, we built a multivariable multilevel model of functional-metabolic associations, assessing multiple functional features, describing the 1) rs-fMRI signal, 2) hemodynamic response, 3) static and 4) time-varying functional connectivity, as predictors of the human brain's metabolic architecture. The full model was trained on one dataset and tested on the other to assess its reproducibility. We found that functional-metabolic spatial coupling is nonlinear and heterogeneous across the brain, and that local measures of rs-fMRI activity and synchrony are more tightly coupled to local metabolism. In the testing dataset, the degree of functional-metabolic spatial coupling was also related to peripheral metabolism. Overall, although a significant proportion of regional metabolic variability can be described by measures of spontaneous activity, additional efforts are needed to explain the remaining variance in the brain's 'dark energy'.

Molecular-enriched functional connectivity in the human brain using multiband multi-echo simultaneous ASL/BOLD fMRI

Receptor-enriched analysis of functional connectivity by targets (REACT) is a strategy to enrich functional MRI (fMRI) data with molecular information on the neurotransmitter distribution density in the human brain, providing a biological basis to the functional connectivity (FC) analysis. Although this approach has been used in BOLD fMRI studies only so far, extending its use to ASL imaging would provide many advantages, including the more direct link of ASL with neuronal activity compared to BOLD and its suitability for pharmacological MRI studies assessing drug effects on baseline brain function. Here, we applied REACT to simultaneous ASL/BOLD resting-state fMRI data of 29 healthy subjects and estimated the ASL and BOLD FC maps related to six molecular systems. We then compared the ASL and BOLD FC maps in terms of spatial similarity, and evaluated and compared the test-retest reproducibility of each modality. We found robust spatial patterns of molecular-enriched FC for both modalities, moderate similarity between BOLD and ASL FC maps and comparable reproducibility for all but one molecular-enriched functional networks. Our findings showed that ASL is as informative as BOLD in detecting functional circuits associated with specific molecular pathways, and that the two modalities may provide complementary information related to these circuits.

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.

Linking resting-state network fluctuations with systems of coherent synaptic density: A multimodal fMRI and 11C-UCB-J PET study

Introduction: Resting-state network (RSN) connectivity is a widely used measure of the brain's functional organization in health and disease; however, little is known regarding the underlying neurophysiology of RSNs. The aim of the current study was to investigate associations between RSN connectivity and synaptic density assessed using the synaptic vesicle glycoprotein 2A radioligand 11C-UCB-J PET. Methods: Independent component analyses (ICA) were performed on resting-state fMRI and PET data from 34 healthy adult participants (16F, mean age: 46 ± 15 years) to identify a priori RSNs of interest (default-mode, right frontoparietal executive-control, salience, and sensorimotor networks) and select sources of 11C-UCB-J variability (medial prefrontal, striatal, and medial parietal). Pairwise correlations were performed to examine potential intermodal associations between the fractional amplitude of low-frequency fluctuations (fALFF) of RSNs and subject loadings of 11C-UCB-J source networks both locally and along known anatomical and functional pathways. Results: Greater medial prefrontal synaptic density was associated with greater fALFF of the anterior default-mode, posterior default-mode, and executive-control networks. Greater striatal synaptic density was associated with greater fALFF of the anterior default-mode and salience networks. Post-hoc mediation analyses exploring relationships between aging, synaptic density, and RSN activity revealed a significant indirect effect of greater age on fALFF of the anterior default-mode network mediated by the medial prefrontal 11C-UCB-J source. Discussion: RSN functional connectivity may be linked to synaptic architecture through multiple local and circuit-based associations. Findings regarding healthy aging, lower prefrontal synaptic density, and lower default-mode activity provide initial evidence of a neurophysiological link between RSN activity and local synaptic density, which may have relevance in neurodegenerative and psychiatric disorders.

Energy constraints on brain network formation

Energy constraints are a fundamental limitation of the brain, which is physically embedded in a restricted space. The collective dynamics of neurons through connections enable the brain to achieve rich functionality, but building connections and maintaining activity come at a high cost. The effects of reducing these costs can be found in the characteristic structures of the brain network. Nevertheless, the mechanism by which energy constraints affect the organization and formation of the neuronal network in the brain is unclear. Here, it is shown that a simple model based on cost minimization can reproduce structures characteristic of the brain network. With reference to the behavior of neurons in real brains, the cost function was introduced in an activity-dependent form correlating the activity cost and the wiring cost as a simple ratio. Cost reduction of this ratio resulted in strengthening connections, especially at highly activated nodes, and induced the formation of large clusters. Regarding these network features, statistical similarity was confirmed by comparison to connectome datasets from various real brains. The findings indicate that these networks share an efficient structure maintained with low costs, both for activity and for wiring. These results imply the crucial role of energy constraints in regulating the network activity and structure of the brain.

Covariance statistics and network analysis of brain PET imaging studies

The analysis of structural and functional neuroimaging data using graph theory has increasingly become a popular approach for visualising and understanding anatomical and functional relationships between different cerebral areas. In this work we applied a network-based approach for brain PET studies using population-based covariance matrices, with the aim to explore topological tracer kinetic differences in cross-sectional investigations. Simulations, test-retest studies and applications to cross-sectional datasets from three different tracers ([18F]FDG, [18F]FDOPA and [11C]SB217045) and more than 400 PET scans were investigated to assess the applicability of the methodology in healthy controls and patients. A validation of statistics, including the assessment of false positive differences in parametric versus permutation testing, was also performed. Results showed good reproducibility and general applicability of the method within the range of experimental settings typical of PET neuroimaging studies, with permutation being the method of choice for the statistical analysis. The use of graph theory for the quantification of [18F]FDG brain PET covariance, including the definition of an entropy metric, proved to be particularly relevant for Alzheimer's disease, showing an association with the progression of the pathology. This study shows that covariance statistics can be applied to PET neuroimaging data to investigate the topological characteristics of the tracer kinetics and its related targets, although sensitivity to experimental variables, group inhomogeneities and image resolution need to be considered when the method is applied to cross-sectional studies.