Skip to content
DraconiticResearch
Search Sources API
181,713 studies31 sourcesUpdated 28 min ago
PreprintCC BYbioRxiv

Mapping the Trade-Offs That Shape Mammalian Brain Network Architecture

  1. 1Technical University of Munich

Abstract

A brain's wiring must satisfy conflicting pressures, from the outside world (be capable) and from the inside (stay within finite resources). These pressures often have opposing requirements. Prior work studies these trade-offs mostly between 2-3 pressures at a time, and thus cannot say how many pressures shape brain wiring or for which trade-off brains decide. To see all possible trade-offs at once and to uncover the set of most important pressures, we generate 25,000 synthetic brain networks and describe each by 29 network properties. We then place 635 human (ages 6-22) and 224 mammalian (12 taxonomic orders) brain networks in this property space. The space is triangular, and follows three pressures: computational capability, robustness to damage, and metabolic efficiency. No network is optimal at all three, and biological brains cluster in one small niche of this triangle. There, they accept higher metabolic cost to gain both robustness and capability. Development and evolution move brains in the same direction; additionally, human maturation first raises capability, and later optimizes for efficiency. This study underscores the triangular trade-off between metabolic efficiency, computational capacity and robustness, and the narrow region brains occupy in this trade-off triangle, visible only from a multi-property, cross-species perspective.

Related

  1. Species-specific WNT requirements shape somite morphogenesis in mammalian embryo models
    C. Knight, L. Simpson, C. Allegrucci +2
    bioRxivPreprintCC BYFull textDevelopmental biologybioRxiv
  2. The mammalian regulatory architecture: biobehavioral portals and the organization of physiological state
    Stephen W. Porges
    Frontiers in NeurosciencePeer-reviewedCC BY 4.0Full textNeuroscienceCrossref
  3. Architecture-dependent Effects of Trade-offs on Evolutionary Navigability and Epistasis
    Nandita Chaturvedi
    arXivPreprintCC BY 4.0Full textEvolutionary biologyBiological & medical physicsarXiv
  4. Individual olfactory channels shape distinct parameters of sleep architecture
    Oliver M. Cook, Benjamin T. Pursley, Garrett Pauls +5
    iSciencePeer-reviewedCC BY-NC 4.0NeuroscienceCrossrefCell Press
  5. Disentangling shape and size variation to compare canopy architecture in maize
    K. Johnson, C. Fournier, C. Palaffre +1
    bioRxivPreprintCC BYFull textPlant biologybioRxiv
BibTeX
@misc{dendorfer2026mapping,
  title = {{Mapping the Trade-Offs That Shape Mammalian Brain Network Architecture}},
  author = {A. Dendorfer and A. Mousley and F. Pascoa dos Santos and D. Astle and K. Fakhar},
  howpublished = {bioRxiv},
  year = {2026},
  month = oct,
  doi = {10.64898/2026.10.07.757032},
  url = {https://www.biorxiv.org/content/10.64898/2026.10.07.757032v1},
  note = {CC BY},
}
RIS
TY  - UNPB
TI  - Mapping the Trade-Offs That Shape Mammalian Brain Network Architecture
AU  - A. Dendorfer
AU  - A. Mousley
AU  - F. Pascoa dos Santos
AU  - D. Astle
AU  - K. Fakhar
PY  - 2026
DA  - 2026/10/07
PB  - bioRxiv
DO  - 10.64898/2026.10.07.757032
UR  - https://www.biorxiv.org/content/10.64898/2026.10.07.757032v1
AB  - A brain's wiring must satisfy conflicting pressures, from the outside world (be capable) and from the inside (stay within finite resources). These pressures often have opposing requirements. Prior work studies these trade-offs mostly between 2-3 pressures at a time, and thus cannot say how many pressures shape brain wiring or for which trade-off brains decide. To see all possible trade-offs at once and to uncover the set of most important pressures, we generate 25,000 synthetic brain networks and describe each by 29 network properties. We then place 635 human (ages 6-22) and 224 mammalian (12 taxonomic orders) brain networks in this property space. The space is triangular, and follows three pressures: computational capability, robustness to damage, and metabolic efficiency. No network is optimal at all three, and biological brains cluster in one small niche of this triangle. There, they accept higher metabolic cost to gain both robustness and capability. Development and evolution move brains in the same direction; additionally, human maturation first raises capability, and later optimizes for efficiency. This study underscores the triangular trade-off between metabolic efficiency, computational capacity and robustness, and the narrow region brains occupy in this trade-off triangle, visible only from a multi-property, cross-species perspective.
ER  -