Variability of regional glucose metabolism and the topology of functional networks in the human 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 predictive nature of spontaneous brain activity across scales and species

Emerging research suggests the brain operates as a "prediction machine," continuously anticipating sensory, motor, and cognitive outcomes. Central to this capability is the brain's spontaneous activity-ongoing internal processes independent of external stimuli. Neuroimaging and computational studies support that this activity is integral to maintaining and refining mental models of our environment, body, and behaviors, akin to generative models in computation. During rest, spontaneous activity expands the variability of potential representations, enhancing the accuracy and adaptability of these models. When performing tasks, internal models direct brain regions to anticipate sensory and motor states, optimizing performance. This review synthesizes evidence from various species, from C. elegans to humans, highlighting three key aspects of spontaneous brain activity's role in prediction: the similarity between spontaneous and task-related activity, the encoding of behavioral and interoceptive priors, and the high metabolic cost of this activity, underscoring prediction as a fundamental function of brains across species.

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.

The metabolic costs of cognition

Cognition and behavior are emergent properties of brain systems that seek to maximize complex and adaptive behaviors while minimizing energy utilization. Different species reconcile this trade-off in different ways, but in humans the outcome is biased towards complex behaviors and hence relatively high energy use. However, even in energy-intensive brains, numerous parsimonious processes operate to optimize energy use. We review how this balance manifests in both homeostatic processes and task-associated cognition. We also consider the perturbations and disruptions of metabolism in neurocognitive diseases.

Simultaneous noninvasive quantification of redox and downstream glycolytic fluxes reveals compartmentalized brain metabolism

Brain metabolism across anatomic regions and cellular compartments plays an integral role in many aspects of neuronal function. Changes in key metabolic pathway fluxes, including oxidative and reductive energy metabolism, have been implicated in a wide range of brain diseases. Given the complex nature of the brain and the need for understanding compartmentalized metabolism noninvasively in vivo, new tools are required. Herein, using hyperpolarized (HP) magnetic resonance imaging coupled with in vivo isotope tracing, we develop a platform to simultaneously probe redox and energy metabolism in the murine brain. By combining HP dehydroascorbate and pyruvate, we are able to visualize increased lactate production in the white matter and increased redox capacity in the deep gray matter. Leveraging positional labeling, we show differences in compartmentalized tricarboxylic acid cycle entry versus downstream flux to glutamate. These findings lay the foundation for clinical translation of the proposed approach to probe brain metabolism.

Impaired brain glucose metabolism in glucagon-like peptide-1 receptor knockout mice

BACKGROUND

Quantitative mapping of the brain's metabolism is a critical tool in studying and diagnosing many conditions, from obesity to neurodegenerative diseases. In particular, noninvasive approaches are urgently required. Recently, there have been promising drug development approaches for the treatment of disorders related to glucose metabolism in the brain and, therefore, against obesity-associated diseases. One of the most important drug targets to emerge has been the Glucagon-like peptide-1 (GLP-1) and its receptor (GLP-1R). GLP and GLP-1R play an important role in regulating blood sugar and maintaining energy homeostasis. However, the macroscopic effects on brain metabolism and function due to the presence of GLP-1R are unclear.

METHODS

To explore the physiological role of GLP-1R in mouse brain glucose metabolism, and its relationship to brain function, we used three methods. We used deuterium magnetic resonance spectroscopy (DMRS) to provide quantitative information about metabolic flux, fluorodeoxyglucose positron emission tomography (FDG-PET) to measure brain glucose metabolism, and resting state-functional MRI (rs-fMRI) to measure brain functional connectivity. We used these methods in both mice with complete GLP-1R knockout (GLP-1R KO) and wild-type C57BL/6N (WT) mice.

RESULTS

The metabolic rate of GLP-1R KO mice was significantly slower than that of WT mice (p = 0.0345, WT mice 0.02335 ± 0.057 mM/min, GLP-1R KO mice 0.01998 ± 0.07 mM/min). Quantification of the mean [18F]FDG signal in the whole brain also showed significantly reduced glucose uptake in GLP-1R KO mice versus control mice (p = 0.0314). Observing rs-fMRI, the functional brain connectivity in GLP-1R KO mice was significantly lower than that in the WT group (p = 0.0032 for gFCD, p = 0.0002 for whole-brain correlation, p < 0.0001 for ALFF).

CONCLUSIONS

GLP-1R KO mice exhibit impaired brain glucose metabolism to high doses of exogenous glucose, and they also have reduced functional connectivity. This suggests that the GLP-1R KO mouse model may serve as a model for correlated metabolic and functional connectivity loss.

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'.

The pons as an optimal background reference region for spinal 18F-FET PET/MRI evaluation

BACKGROUND

This study aims to evaluate the effect of various background reference regions on spinal 18F-FET PET imaging, with a focus on distinguishing between spinal tumors and myelitis. To enhance diagnostic accuracy, we investigated the pons and several other spinal cord area as potential references, given the challenges in interpreting spinal PET results.

RESULTS

A retrospective analysis was conducted on 30 patients, 15 with cervical myelitis and 15 with cervical tumors, who underwent O-(2-[18F]-fluoroethyl)-L-tyrosine (FET) PET/MR imaging. The stability of uptake across four regions, including the pons, C2, C2-C7, and T1-T3, was compared. The standardized uptake value ratio (SUVR) was then evaluated using various background regions, and their effectiveness in differentiating between spinal tumors and myelitis was compared. Additionally, we correlated the SUVR values derived from these regions with the Ki-67 proliferation index in tumor patients. The study found no significant difference in SUVmax (U = 110, p = 0.93) and SUVmean (U = 89, p = 0.35) values at lesion sites between myelitis and tumor patients. The pons had the highest average uptake (p < 0.001) compared to the other three regions. However, its coefficient of variation (CV) was significantly lower than that of the C2-C7 (p < 0.0001) and T1-T3 segments (p < 0.05). The SUVRmax values, calculated using the regions of pons, C2-C7 and T1-T3, were found to significantly differentiate between tumors and myelitis (p < 0.05). However, only the pons-based SUVRmean was able to significantly distinguish between the two groups (p < 0.05). Additionally, the pons-based SUVRmax (r = 0.63, p = 0.013) and SUVRmean (r = 0.67, p = 0.007) demonstrated a significant positive correlation with the Ki-67 index.

CONCLUSIONS

This study suggests that the pons may be considered a suitable reference region for spinal 18F-FET PET imaging, which can improve the differentiation between spinal tumors and myelitis. The significant correlation between pons-based SUVR values and the Ki-67 index further highlights the potential of this approach in assessing tumor cell proliferation.

Synaptic signaling modeled by functional connectivity predicts metabolic demands of the human brain

PURPOSE

The human brain is characterized by interacting large-scale functional networks fueled by glucose metabolism. Since former studies could not sufficiently clarify how these functional connections shape glucose metabolism, we aimed to provide a neurophysiologically-based approach.

METHODS

51 healthy volunteers underwent simultaneous PET/MRI to obtain BOLD functional connectivity and [18F]FDG glucose metabolism. These multimodal imaging proxies of fMRI and PET were combined in a whole-brain extension of metabolic connectivity mapping. Specifically, functional connectivity of all brain regions were used as input to explain glucose metabolism of a given target region. This enabled the modeling of postsynaptic energy demands by incoming signals from distinct brain regions.

RESULTS

Functional connectivity input explained a substantial part of metabolic demands but with pronounced regional variations (34 - 76%). During cognitive task performance this multimodal association revealed a shift to higher network integration compared to resting state. In healthy aging, a dedifferentiation (decreased segregated/modular structure of the brain) of brain networks during rest was observed. Furthermore, by including data from mRNA maps, [11C]UCB-J synaptic density and aerobic glycolysis (oxygen-to-glucose index from PET data), we show that whole-brain functional input reflects non-oxidative, on-demand metabolism of synaptic signaling. The metabolically-derived directionality of functional inputs further marked them as top-down predictions. In addition, the approach uncovered formerly hidden networks with superior efficiency through metabolically informed network partitioning.

CONCLUSIONS

Applying multimodal imaging, we decipher a crucial part of the metabolic and neurophysiological basis of functional connections in the brain as interregional on-demand synaptic signaling fueled by anaerobic metabolism. The observed task- and age-related effects indicate promising future applications to characterize human brain function and clinical alterations.

Prediction of Cognitive Progression Due to Alzheimer's Disease in Normal Participants Based on Individual Default Mode Network Metabolic Connectivity Strength

BACKGROUND

Predicting cognitive decline among individuals in the aging population who are already amyloid-β (Aβ) positive or tau positive poses clinical challenges. In Alzheimer's disease research, intra-default mode network (DMN) connections play a pivotal role in diagnosis. In this article, we propose metabolic connectivity within the DMN as a supplementary biomarker to the Aβ, pathological tau, and neurodegeneration framework.

METHODS

Extracting data from 1292 participants in the Alzheimer's Disease Neuroimaging Initiative, we collected paired T1-weighted structural magnetic resonance imaging and 18F-labeled-fluorodeoxyglucose positron emission computed tomography scans. Individual metabolic DMN networks were constructed, and metabolic connectivity (MC) strength in the DMN was assessed. In the cognitively unimpaired group, the Cox model identified cognitively unimpaired (MC+), high-risk participants, with Kaplan-Meier survival analyses and hazard ratios revealing the strength of MC's predictive performance. Spearman correlation analyses explored relationships between MC strength, and Aβ, pathological tau, neurodegeneration biomarkers, and clinical scales. DMN standard uptake value ratio (SUVR) provided comparative insights in the analyses.

RESULTS

Both MC strength and SUVR exhibited gradual declines with cognitive deterioration, displaying significant intergroup differences. Survival analyses indicated enhanced Aβ and tau prediction with both metrics, with MC strength outperforming SUVR. Combined MC strength and Aβ yielded optimal predictive performance (hazard ratio = 9.29), followed by MC strength and tau (hazard ratio = 8.92). Generally, the strength of MC's correlations with Aβ, pathological tau, and neurodegeneration biomarkers exceeded SUVR.

CONCLUSIONS

Individuals with normal cognition and disrupted DMN metabolic connectivity face an elevated risk of cognitive decline linked to Aβ that precedes metabolic issues.

Discernible interindividual patterns of global efficiency decline during theoretical brain surgery

The concept of functional localization within the brain and the associated risk of resecting these areas during removal of infiltrating tumors, such as diffuse gliomas, are well established in neurosurgery. Global efficiency (GE) is a graph theory concept that can be used to simulate connectome disruption following tumor resection. Structural connectivity graphs were created from diffusion tractography obtained from the brains of 80 healthy adults. These graphs were then used to simulate parcellation resection in every gross anatomical region of the cerebrum by identifying every possible combination of adjacent nodes in a graph and then measuring the drop in GE following nodal deletion. Progressive removal of brain parcellations led to patterns of GE decline that were reasonably predictable but had inter-subject differences. Additionally, as expected, there were deletion of some nodes that were worse than others. However, in each lobe examined in every subject, some deletion combinations were worse for GE than removing a greater number of nodes in a different region of the brain. Among certain patients, patterns of common nodes which exhibited worst GE upon removal were identified as "connectotypes". Given some evidence in the literature linking GE to certain aspects of neuro-cognitive abilities, investigating these connectotypes could potentially mitigate the impact of brain surgery on cognition.

A metabolism-functional connectome sparse coupling method to reveal imaging markers for Alzheimer's disease based on simultaneous PET/MRI scans

Abnormal glucose metabolism and hemodynamic changes in the brain are closely related to cognitive function, providing complementary information from distinct biochemical and physiological processes. However, it remains unclear how to effectively integrate these two modalities across distinct brain regions. In this study, we developed a connectome-based sparse coupling method for hybrid PET/MRI imaging, which could effectively extract imaging markers of Alzheimer's disease (AD) in the early stage. The FDG-PET and resting-state fMRI data of 56 healthy controls (HC), 54 subjective cognitive decline (SCD), and 27 cognitive impairment (CI) participants due to AD were obtained from SILCODE project (NCT03370744). For each participant, the metabolic connectome (MC) was constructed by Kullback-Leibler divergence similarity estimation, and the functional connectome (FC) was constructed by Pearson correlation. Subsequently, we measured the coupling strength between MC and FC at various sparse levels, assessed its stability, and explored the abnormal coupling strength along the AD continuum. Results showed that the sparse MC-FC coupling index was stable in each brain network and consistent across subjects. It was more normally distributed than other traditional indexes and captured more SCD-related brain areas, especially in the limbic and default mode networks. Compared to other traditional indices, this index demonstrated best classification performance. The AUC values reached 0.748 (SCD/HC) and 0.992 (CI/HC). Notably, we found a significant correlation between abnormal coupling strength and neuropsychological scales (p < .05). This study provides a clinically relevant tool for hybrid PET/MRI imaging, allowing for exploring imaging markers in early stage of AD and better understanding the pathophysiology along the AD continuum.

The technology, opportunities, and challenges of Synthetic Biological Intelligence

Integrating neural cultures developed through synthetic biology methods with digital computing has enabled the early development of Synthetic Biological Intelligence (SBI). Recently, key studies have emphasized the advantages of biological neural systems in some information processing tasks. However, neither the technology behind this early development, nor the potential ethical opportunities or challenges, have been explored in detail yet. Here, we review the key aspects that facilitate the development of SBI and explore potential applications. Considering these foreseeable use cases, various ethical implications are proposed. Ultimately, this work aims to provide a robust framework to structure ethical considerations to ensure that SBI technology can be both researched and applied responsibly.

Multiplex core of the human brain using structural, functional and metabolic connectivity derived from hybrid PET-MR imaging

With the increasing success of mapping brain networks and availability of multiple MR- and PET-based connectivity measures, the need for novel methodologies to unravel the structure and function of the brain at multiple spatial and temporal scales is emerging. Therefore, in this work, we used hybrid PET-MR data of healthy volunteers (n = 67) to identify multiplex core nodes in the human brain. First, monoplex networks of structural, functional and metabolic connectivity were constructed, and consequently combined into a multiplex SC-FC-MC network by linking the same nodes categorically across layers. Taking into account the multiplex nature using a tensorial approach, we identified a set of core nodes in this multiplex network based on a combination of eigentensor centrality and overlapping degree. We introduced a coreness coefficient, which mitigates the effect of modeling parameters to obtain robust results. The proposed methodology was applied onto young and elderly healthy volunteers, where differences observed in the monoplex networks persisted in the multiplex as well. The multiplex core showed a decreased contribution to the default mode and salience network, while an increased contribution to the dorsal attention and somatosensory network was observed in the elderly population. Moreover, a clear distinction in eigentensor centrality was found between young and elderly healthy volunteers.

Spatiotemporal topological correspondence between blood oxygenation and glucose metabolism revealed by simultaneous fPET-fMRI in brain's white matter

White matter (WM) makes up half of the human brain. Compelling functional MRI evidence indicates that white matter exhibits neural activation and synchronization via a hemodynamic window. However, the neurometabolic underpinnings of white matter temporal synchronization and spatial topology remain unknown. By leveraging concurrent [18F]FDG-fPET and blood-oxygenation-level-dependent-fMRI, we demonstrated the temporal and spatial correspondences between blood oxygenation and glucose metabolism in the human brain white matter. In the temporal scale, we found that blood-oxygenation-level-dependent signals shared mutual information with FDG signals in the default-mode, visual, and sensorimotor-auditory networks. For spatial distribution, the blood-oxygenation-level-dependent functional networks in white matter were accompanied by substantial correspondence of FDG functional connectivity at different topological scales, including degree centrality and global gradients. Furthermore, the content of blood-oxygenation-level-dependent fluctuations in the white matter default-mode network was aligned and liberal with the FDG graph, suggesting the freedom of default-mode network neuro-dynamics, but the constraint by metabolic dynamics. Moreover, the dissociation of the functional gradient between blood-oxygenation-level-dependent and FDG connectivity specific to the white matter default-mode network revealed functional heterogeneities. Together, the results showed that brain energy metabolism was closely coupled with blood oxygenation in white matter. Comprehensive and complementary information from fMRI and fPET might therefore help decode brain white matter functions.

Regional contribution of vascular dysfunction in white matter dementia: clinical and neuropathological insights

The maintenance of adequate blood supply and vascular integrity is fundamental to ensure cerebral function. A wide range of studies report vascular dysfunction in white matter dementias, a group of cerebral disorders characterized by substantial white matter damage in the brain leading to cognitive impairment. Despite recent advances in imaging, the contribution of vascular-specific regional alterations in white matter dementia has been not extensively reviewed. First, we present an overview of the main components of the vascular system involved in the maintenance of brain function, modulation of cerebral blood flow and integrity of the blood-brain barrier in the healthy brain and during aging. Second, we review the regional contribution of cerebral blood flow and blood-brain barrier disturbances in the pathogenesis of three distinct conditions: the archetypal white matter predominant neurocognitive dementia that is vascular dementia, a neuroinflammatory predominant disease (multiple sclerosis) and a neurodegenerative predominant disease (Alzheimer's). Finally, we then examine the shared landscape of vascular dysfunction in white matter dementia. By emphasizing the involvement of vascular dysfunction in the white matter, we put forward a hypothetical map of vascular dysfunction during disease-specific progression to guide future research aimed to improve diagnostics and facilitate the development of tailored therapies.

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.