– demonstrated that the optimal component placement which minimizes total wiring cost agrees with results from specific regions of the neocortex. Several studies at the level of small and specific neuronal populations have reported preference for low wiring cost in the brain. Network analysis of macaque brain data appear to support wiring economy principle, albeit partially. Computational modeling studies showed that graphs computationally evolved for high complexity of dynamic behavior had a sparse, small-world topology of edges between connected nodes. Van Essen and Stevens first conjectured that axonal tension might cause strongly connected regions to pull towards each other during development, thereby leading to compact neural circuitry and short wiring length. The metabolic costs of building and functionally resourcing the brain are large in proportion to the total energy budget of the body. The economy of wiring in physical systems has been analyzed from a network viewpoint. The latter property is especially important because it determines the total metabolic cost associated with maintaining such a large scale network. The human brain is believed to have faced evolutionary pressure to optimize various network quantities, for instance information capacity, latency, average shortest path length, clustering tendencies, complexity and wiring cost. Intriguingly, our results also suggest that the brain mainly optimizes for the most desirable network connectivity, and the observed brain anatomy is merely a result of this optimization. This confirms the widely held but poorly tested claim that wiring economy is a driving principle of the brain. We found that beginning from any random configuration, the nodes invariably arrange themselves in a configuration with a striking resemblance to the brain. An efficient minimization routine was written to find the lowest wiring cost configuration. In another set of analyses we kept the observed brain network topology and connectivity but allowed nodes to freely move on a 3D manifold topologically identical to the brain. A specifically designed minimization routine could not find cheaper wiring without significantly degrading network performance. In one set of analyses we found that the connectivity structure of the brain has near optimal wiring cost compared to random networks with the same number of edges, degree distribution and edge weight distribution. Unlike previous studies we use weighted networks, where connections between cortical nodes are real-valued rather than binary off-on connections. We specifically address the question of whether brain anatomy determines its connectivity or vice versa. We investigate this aspect in the human brain using whole brain connectivity networks extracted from high resolution diffusion MRI data of 14 normal volunteers.
![hippocampus anatomy diagram hippocampus anatomy diagram](https://c8.alamy.com/comp/2DBHHGD/brain-anatomy-diagram-with-sectioned-in-different-colours-and-named-2DBHHGD.jpg)
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Minimization of the wiring cost of white matter fibers in the human brain appears to be an organizational principle.