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@ARTICLE{Oldham:911393,
      author       = {Oldham, Stuart and Fulcher, Ben D. and Aquino, Kevin and
                      Arnatkevičiūtė, Aurina and Paquola, Casey and Shishegar,
                      Rosita and Fornito, Alex},
      title        = {{M}odeling spatial, developmental, physiological, and
                      topological constraints on human brain connectivity},
      journal      = {Science advances},
      volume       = {8},
      number       = {22},
      issn         = {2375-2548},
      address      = {Washington, DC [u.a.]},
      publisher    = {Assoc.},
      reportid     = {FZJ-2022-04676},
      pages        = {eabm6127},
      year         = {2022},
      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 that more accurately captures 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 interregional transcriptional or microstructural
                      similarity rather than topological wiring rules. However,
                      all models struggled to capture topographical (i.e.,
                      spatial) network properties. 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},
      ddc          = {500},
      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)16},
      pubmed       = {35658036},
      UT           = {WOS:000808053900015},
      doi          = {10.1126/sciadv.abm6127},
      url          = {https://juser.fz-juelich.de/record/911393},
}