Hauptseite > Publikationsdatenbank > Modeling spatial, developmental, physiological, and topological constraints on human brain connectivity > print

001     903464
005     20211219012042.0
024 7 _ |2 doi
|a 10.1101/2021.09.29.462379
024 7 _ |2 Handle
|a 2128/29434
024 7 _ |a altmetric:114342382
|2 altmetric
037 _ _ |a FZJ-2021-05137
100 1 _ |0 P:(DE-HGF)0
|a Oldham, S.
|b 0
|e Corresponding author
245 _ _ |a Modeling spatial, developmental, physiological, and topological constraints on human brain connectivity
260 _ _ |c 2021
336 7 _ |0 PUB:(DE-HGF)25
|2 PUB:(DE-HGF)
|a Preprint
|b preprint
|m preprint
|s 1639130732_24306
336 7 _ |2 ORCID
|a WORKING_PAPER
336 7 _ |0 28
|2 EndNote
|a Electronic Article
336 7 _ |2 DRIVER
|a preprint
336 7 _ |2 BibTeX
|a ARTICLE
336 7 _ |2 DataCite
|a Output Types/Working Paper
520 _ _ |a 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.
536 _ _ |0 G:(DE-HGF)POF4-5254
|a 5254 - Neuroscientific Data Analytics and AI (POF4-525)
|c POF4-525
|f POF IV
|x 0
536 _ _ |0 G:(DE-HGF)InterLabs-0015
|a HIBALL - Helmholtz International BigBrain Analytics and Learning Laboratory (HIBALL) (InterLabs-0015)
|c InterLabs-0015
|x 1
588 _ _ |a Dataset connected to CrossRef
700 1 _ |0 0000-0002-3003-4055
|a Fulcher, B. D.
|b 1
700 1 _ |0 0000-0002-7435-0236
|a Aquino, K.
|b 2
700 1 _ |0 0000-0003-4098-7084
|a Arnatkevičiūtė, A.
|b 3
700 1 _ |0 P:(DE-Juel1)187055
|a Paquola, C.
|b 4
|u fzj
700 1 _ |0 P:(DE-HGF)0
|a Shishegar, R.
|b 5
700 1 _ |0 P:(DE-HGF)0
|a Fornito, A.
|b 6
773 _ _ |a 10.1101/2021.09.29.462379
856 4 _ |u https://juser.fz-juelich.de/record/903464/files/Oldham_etal_2021.09.29.462379v1.full.pdf
|y OpenAccess
909 C O |o oai:juser.fz-juelich.de:903464
|p openaire
|p open_access
|p VDB
|p driver
|p dnbdelivery
910 1 _ |0 I:(DE-588b)5008462-8
|6 P:(DE-Juel1)187055
|a Forschungszentrum Jülich
|b 4
|k FZJ
913 1 _ |0 G:(DE-HGF)POF4-525
|1 G:(DE-HGF)POF4-520
|2 G:(DE-HGF)POF4-500
|3 G:(DE-HGF)POF4
|4 G:(DE-HGF)POF
|9 G:(DE-HGF)POF4-5254
|a DE-HGF
|b Key Technologies
|l Natural, Artificial and Cognitive Information Processing
|v Decoding Brain Organization and Dysfunction
|x 0
914 1 _ |y 2021
915 _ _ |0 StatID:(DE-HGF)0510
|2 StatID
|a OpenAccess
915 _ _ |0 LIC:(DE-HGF)CCBY4
|2 HGFVOC
|a Creative Commons Attribution CC BY 4.0
920 1 _ |0 I:(DE-Juel1)INM-1-20090406
|k INM-1
|l Strukturelle und funktionelle Organisation des Gehirns
|x 0
980 1 _ |a FullTexts
980 _ _ |a preprint
980 _ _ |a VDB
980 _ _ |a UNRESTRICTED
980 _ _ |a I:(DE-Juel1)INM-1-20090406

LibraryCollectionCLSMajorCLSMinorLanguageAuthor
Marc 21