Large-Scale Brain Networks Track Gasoline Price Shifts

Macroeconomic conditions shape daily constraints and perceived environment, yet it remains unclear whether real-world economic dynamics are reflected in intrinsic brain organization at the population level. Here, I leveraged the Human Connectome Project (HCP) acquisition timeline as a naturalistic sampling frame to test whether macroeconomic time series vary with large-scale resting-state networks. Resting-state fMRI from 726 healthy young adults was aligned to quarterly macroeconomic indicators using the HCP "Quarter" variable. I quantified intrinsic organization using network functional connectivity (FC) and network-level amplitude of low-frequency fluctuations (ALFF). I implemented (1) a quasi-natural experiment contrasting a pre- vs post-gasoline-price shock (collapse) cohort, and (2) quarter-level partial correlations between network measures and four macro indicators, including gasoline price, consumer sentiment, unemployment, and stock market return. Post-shock participants showed significantly higher within-network FC across all seven networks and selective increases in between-network coupling concentrated among sensory and attention systems, as well as limbic-default interactions. ALFF exhibited bidirectional shifts: Visual ALFF increased post-shock, whereas Limbic and Frontoparietal ALFF were higher pre-shock. Quarter-level analyses mirrored this differential pattern, with gas price positively associated with Limbic and Frontoparietal ALFF and negatively with Visual and Dorsal Attention ALFF. Across macro indicators, gasoline price and consumer sentiment showed the most widespread FC associations, with largely opposing correlation signatures, while unemployment and equity returns were comparatively weak after correction. These findings suggest that salient, behaviorally proximal macroeconomic dynamics, particularly energy price variation, track population-level differences in intrinsic brain network architecture, motivating future work with finer-grained timing and individual-level exposure measures to strengthen causal inference.