The Brain as a Low-Resistance Pathway Network: A Cross-Domain Evidence Synthesis from White Matter Microstructure, Sleep Deprivation, and Default Mode Network Metabolism

The brain's lifelong decline in white matter integrity, the cognitive impairments caused by sleep deprivation, and the high energy use of the default mode network can be understood as a single physical process: the brain is a network of low-resistance pathways formed by charge flow, and memory is those pathways themselves. Analysis of UK Biobank data (N=21,236) shows white matter hyperintensity volume independently predicts cognitive decline beyond gray matter atrophy. Sleep deprivation impairs cognitive domains in proportion to their reliance on long-range brain integration, with sustained vigilance most affected (g=−0.776) and reasoning spared (g=−0.125, n.s.). Default mode network connectivity strength independently predicts local glucose metabolism (r=0.62), suggesting its high energy use reflects physical pathway maintenance rather than thought.

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Abstract

The core physical attributes of the brain—the lifelong decline of white matter microstructural integrity, the selective cognitive impairment caused by sleep deprivation, and the high resting-state energy consumption of the default mode network—are currently dispersed across independent domains of neuroimaging, sleep science, and cognitive neuroscience. This paper proposes that these phenomena can be coherently organized within a single physical framework: the brain is the physical region with the densest low-resistance pathways deposited by charge flow, and memory is not a function of the brain but the low-resistance pathways themselves. Three categories of publicly available evidence are synthesized to evaluate this proposal. First, published mediation analyses from UK Biobank (N=21,236) demonstrate that white matter hyperintensity volume independently predicts cognitive decline after controlling for gray matter atrophy, ruling out the alternative explanation that white matter damage is merely an epiphenomenon of generalized neurodegeneration. Second, meta-analytic data from Lim and Dinges (2010) reveal that sleep deprivation impairs cognitive domains in strict proportion to their dependence on long-range inter-regional integration: simple sustained vigilance (g=−0.776, the domain most reliant on global cross-brain synchrony) shows the largest impairment, while reasoning accuracy (g=−0.125, n.s.) is spared. This gradient is reversed in acute hypoglycemia and alcohol intoxication—metabolic stressors that preferentially impair local processing—demonstrating that sleep deprivation selectively targets long-range low-resistance pathways rather than producing a generalized cognitive deficit. Third, multimodal PET-fMRI data show that functional connectivity strength within the default mode network independently predicts local glucose metabolism (r=0.62, remaining r=0.51 after controlling for gray matter volume and cerebral blood flow), ruling out the alternative explanation that DMN hypermetabolism merely reflects ongoing introspective thought. The convergence of physical pointers from three independent domains—structural, functional, and metabolic—provides a working framework for understanding the brain as a low-resistance pathway network whose maintenance requires a specific low-load window (sleep) and whose decline follows the physical trajectory of pathway degradation. Specific, falsifiable predictions are identified, and data gaps are explicitly documented. All evidence is drawn from publicly available published sources and can be independently verified.