Relationship between brain energy consumption and brain organizations

The human brain requires a continuous supply of energy to function effectively. Here, we investigated how the low-dimensional organization of intrinsic functional connectivity patterns based on resting-state functional magnetic resonance imaging relates to brain energy expenditure measured by fluorodeoxyglucose positron emission tomography. By incrementally adding more dimensions of brain organization (via functional gradients), we were able to show that increasing amounts of variance in the map of brain energy expenditure could be accounted for. In particular, the brain organization dimensions that explained a large amount of the variance in intrinsic brain function also explained a large amount of the regional variance in the energy expenditure maps. This was particularly true for brain organization maps based on the strongest connections, suggesting that "weak" connections may not explain as much energy variance. Notably, our topological model was more effective than random brain organization configurations, suggesting that brain organization may be specifically associated with energy optimization. Finally, using brain asymmetry as a model for metabolic efficiency, we found that optimizing energy expenditure independently in each hemisphere outperformed non-independent optimization. This supports the concept of hemispheric competition rather than lateralization in energy allocation. Our results demonstrate how the spatial organization of functional connections is systematically linked to optimized energy expenditure in the human brain, providing new insights into the metabolic basis of brain function. 

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’.
 

Sleep entails significant changes in cerebral hemodynamics and metabolism. Yet, how these two processes evolve throughout wakefulness and sleep and their spatiotemporal dependence remain largely unknown. By integrating a new functional PET technique with simultaneous EEG-fMRI, we reveal a tightly coupled temporal evolution of global hemodynamics and metabolism during the descent into NREM sleep, with large hemodynamic fluctuations emerging as global glucose metabolism declines, both of which track EEG arousal dynamics. Furthermore, we identify two distinct network patterns that emerge during NREM sleep: an oscillating, high-metabolism sensorimotor network remains active and dynamic in NREM sleep, whereas hemodynamic and metabolic activity in the default-mode network is suppressed. These results elucidate how sleep produces a loss of awareness while preserving sensory responses, and uncover a complex, alternating balance of hemodynamic and metabolic dynamics in the sleeping brain.
 

A organisational principle of the human brain is a trade-off between minimising energy cost and maximising communication efficiency. Although hub regions are energetically costly, they are especially important for information transfer across the brain because of their long-range connection. Here we investigate the cost of metabolic networks using rates of glucose in hubs relative to their role in information transfer across the brain in 40 younger (mean age 27.9 years; range 20-42) and 46 older (mean 75.8; 60-89) adults. Ageing was associated with lower global integration of metabolic hub regions, indicating disrupted information transfer across the metabolic network in older adults. For younger adults, the frontal hubs were more central, efficient and connected in facilitating global communication across the brain. Greater local efficiency of hubs in younger adults also minimised the cost of maintaining network communication via fewer paths to locally connected nodes. Older adults had a smaller energy budget in comparison to younger adults, and older adults used a higher proportion of energy to support mostly posterior hub regions. The lower efficiency and higher cost of the metabolic network in older adults was associated with worse cognitive performance. We conclude that ageing is associated with an altered metabolic network topology and a high glucose cost in hub regions. Our results highlight the fundamental role that metabolism plays in supporting information transfer in the brain and the unique insights that metabolic connectivity provides into the ageing brain.