Metabolism modulates network synchrony in the aging brain

Brain aging is associated with hypometabolism and global changes in functional connectivity. Using functional MRI (fMRI), we show that network synchrony, a collective property of brain activity, decreases with age. Applying quantitative methods from statistical physics, we provide a generative (Ising) model for these changes as a function of the average communication strength between brain regions. We find that older brains are closer to a critical point of this communication strength, in which even small changes in metabolism lead to abrupt changes in network synchrony. Finally, by experimentally modulating metabolic activity in younger adults, we show how metabolism alone—independent of other changes associated with aging—can provide a plausible candidate mechanism for marked reorganization of brain network topology.

One of the most fundamental questions in neuroscience is how the familiar patterns of collective, brain-wide activity arise from the properties of the constituent neurons and their networks. Here, we study how the brain’s global activity patterns change with age and how those changes might arise from the reduced metabolic activity of constituent regions.We draw on two types of experimental evidence. First, as established using positron emission tomography, older brains show reduced glucose metabolism (1–3). Second, as established by functional MRI (fMRI), aging is associated with weakened functional connectivity (FC; i.e., reduced communication [on average] between brain regions) (4–6). Combining both observations suggests that impaired glucose metabolism may underlie changes in FC (1, 7). Supporting this link are studies showing disruptions similar to those seen with aging in type 2 diabetic subjects (8, 9).In healthy brains, resting-state brain activity (states during which subjects are not engaged in any explicit task) alternates between segregating computations within localized functional domains and integrating this information across these domains (7, 10–13). The metabolic cost of these activities increases in proportion to the number and length of functional connections between pairs of brain regions (14), making highly connected (integrated) networks more energetically costly (10). Moreover, connections with the highest cost are the first to weaken with age (6, 7, 15, 16). Thus, it has been hypothesized that declining glucose metabolism in older brains drives the loss of high-cost (integrated) functional activities (14, 16). Yet, the relationship between energetic constraints at the level of individual regions and the apparent reorganization of the functional connectome is still not well understood.Statistical physics, which interprets the collective properties of complex systems in terms of individual interactions between the underlying parts, provides powerful tools for understanding how brain networks reorganize under constraints (17, 18). In particular, we employ an Ising model (19–21) to describe how pairwise interactions between brain regions give rise to specific profiles of network synchrony, a time-dependent average of the activity over the brain (22–24). The Ising model is a simple yet powerful tool to capture ordered and disordered phases and the transitions between them. It assumes that two things matter in understanding the collective behaviors of a system of agents (brain regions in this case): the topological relationships among the agents and their strength of interaction. This perspective can provide insight into global changes in FC observed in the data by assuming that brain regions are fully connected with a single average interaction strength.While the Ising model provides a general tool for describing the collective properties of complex systems, we adapt it to examine the specific relationship between brain aging and metabolic activity. To achieve this, we analyzed four resting-state fMRI datasets. We utilized two large-scale resting-state 3T (Tesla) fMRI datasets to identify aging related effects: the Cambridge Centre for Aging and Neuroscience Stage II Study (Cam-CAN; ages 18 to 88, N=652) (25) and the Human Connectome Project Aging fMRI dataset (HCP-A; ages 36 to 100, N=712) (26). To assess metabolic effects, we used two 7T fMRI datasets from the Protecting the Aging Brain fMRI data repository: a Bolus dataset (PAgB Bolus; ages 21 to 48, N=38) consisting of a within-subject experiment in which individuals were scanned following separate administration of calorie-matched glucose and ketones (d-β-hydroxybutyrate ketone ester [D-βHb]) and a Diet dataset (PAgB Diet; ages 21 to 43, N=12) consisting of an independent within-subject experiment of healthy, young adults scanned at resting state while on glycolytic and ketogenic diets (27). Ketone bodies decrease the relative free energy of adenosine triphosphate (ATP) production by approximately 27% as compared with glucose (28). The additional efficiency of ketone bodies as a cellular fuel source, observed even in healthy subjects, has been shown to increase both cardiac efficiency (28) as well as brain activity (27).The significance of this work is threefold. First, we demonstrate that the Ising model, with a fully connected topology, provides a mechanism for how FC patterns change in qualitatively abrupt ways as a function of the average interaction between brain regions (18). Second, we establish a direct link between network synchrony and the relative frequencies of integrated (high-cost) and segregated (low-cost) brain activities (10, 14). Finally, we suggest a precise relationship between differences in FC over the life span as well as in response to changes in the brain’s access to energy.