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The human brain displays a complex network topology, whose structuralorganization is widely studied using diffusion tensor imaging. The originalgeometry from which emerges the network topology is known, as well as thelocalization of the network nodes in respect to the brain morphology andanatomy. One of the most challenging problems of current network science is toinfer the latent geometry from the mere topology of a complex network. Thehuman brain structural connectome represents the perfect benchmark to testalgorithms aimed to solve this problem. Coalescent embedding was recentlydesigned to map a complex network in the hyperbolic space, inferring the nodeangular coordinates. Here we show that this methodology is able tounsupervisedly reconstruct the latent geometry of the brain with an incredibleaccuracy and that the intrinsic geometry of the brain networks strongly relatesto the lobes organization known in neuroanatomy. Furthermore, coalescentembedding allowed the detection of geometrical pathological changes in theconnectomes of Parkinson's Disease patients. The present study represents thefirst evidence of brain networks' angular coalescence in the hyperbolic space,opening a completely new perspective, possibly towards the realization oflatent geometry network markers for evaluation of brain disorders andpathologies.

Coalescent embedding in the hyperbolic space unsupervisedly discloses the hidden geometry of the brain