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@ARTICLE{Oldham:903464,
      author       = {Oldham, S. and Fulcher, B. D. and Aquino, K. and
                      Arnatkevičiūtė, A. and Paquola, C. and Shishegar, R. and
                      Fornito, A.},
      title        = {{M}odeling spatial, developmental, physiological, and
                      topological constraints on human brain connectivity},
      reportid     = {FZJ-2021-05137},
      year         = {2021},
      abstract     = {The complex connectivity of nervous systems is thought to
                      have been shaped by competitive selection pressures to
                      minimize wiring costs and support adaptive function.
                      Accordingly, recent modeling work indicates that stochastic
                      processes, shaped by putative trade-offs between the cost
                      and value of each connection, can successfully reproduce
                      many topological properties of macroscale human connectomes
                      measured with diffusion magnetic resonance imaging. Here, we
                      derive a new formalism with the aim to more accurately
                      capture the competing pressures of wiring cost minimization
                      and topological complexity. We further show that model
                      performance can be improved by accounting for developmental
                      changes in brain geometry and associated wiring costs, and
                      by using inter-regional transcriptional or microstructural
                      similarity rather than topological wiring-rules. However,
                      all models struggled to capture topologies spatial
                      embedding. Our findings highlight an important role for
                      genetics in shaping macroscale brain connectivity and
                      indicate that stochastic models offer an incomplete account
                      of connectome organization.},
      cin          = {INM-1},
      cid          = {I:(DE-Juel1)INM-1-20090406},
      pnm          = {5254 - Neuroscientific Data Analytics and AI (POF4-525) /
                      HIBALL - Helmholtz International BigBrain Analytics and
                      Learning Laboratory (HIBALL) (InterLabs-0015)},
      pid          = {G:(DE-HGF)POF4-5254 / G:(DE-HGF)InterLabs-0015},
      typ          = {PUB:(DE-HGF)25},
      doi          = {10.1101/2021.09.29.462379},
      url          = {https://juser.fz-juelich.de/record/903464},
}