4.8 Article

Brain mapping across 16 autism mouse models reveals a spectrum of functional connectivity subtypes

Journal

MOLECULAR PSYCHIATRY
Volume 26, Issue 12, Pages 7610-7620

Publisher

SPRINGERNATURE
DOI: 10.1038/s41380-021-01245-4

Keywords

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Funding

  1. ETH career seed grant [SEED-42 54 16-1]
  2. SNSF AMBIZIONE [PZ00P3_173984/1]
  3. Simons Foundation [SFARI 400101]
  4. European Research Council (ERC) under the European Union [802371]
  5. Brain and Behavior Foundation (NARSAD) [25861]
  6. NIH [1R21MH116473-01A1]
  7. Telethon foundation [GGP19177, GGP19226A]
  8. European Union [GA845065]
  9. PRIN (Ministero dell'Istruzione dell'Universita e della Ricerca) [2017A9MK4R]
  10. CARIPLO Foundation [2017-0886, 2019-1973]
  11. University of Trento Strategic Project TRAIN Trentino Autism Initiative.
  12. [ETH-38 16-2]
  13. European Research Council (ERC) [802371] Funding Source: European Research Council (ERC)
  14. Swiss National Science Foundation (SNF) [PZ00P3_173984] Funding Source: Swiss National Science Foundation (SNF)

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The study reveals that Autism Spectrum Disorder (ASD) is characterized by diverse and highly heterogeneous abnormalities in brain connectivity, with different etiologies causing a broad spectrum of connectional abnormalities that can be classified into four subtypes with discrete signatures of network dysfunction.
Autism Spectrum Disorder (ASD) is characterized by substantial, yet highly heterogeneous abnormalities in functional brain connectivity. However, the origin and significance of this phenomenon remain unclear. To unravel ASD connectopathy and relate it to underlying etiological heterogeneity, we carried out a bi-center cross-etiological investigation of fMRI-based connectivity in the mouse, in which specific ASD-relevant mutations can be isolated and modeled minimizing environmental contributions. By performing brain-wide connectivity mapping across 16 mouse mutants, we show that different ASD-associated etiologies cause a broad spectrum of connectional abnormalities in which diverse, often diverging, connectivity signatures are recognizable. Despite this heterogeneity, the identified connectivity alterations could be classified into four subtypes characterized by discrete signatures of network dysfunction. Our findings show that etiological variability is a key determinant of connectivity heterogeneity in ASD, hence reconciling conflicting findings in clinical populations. The identification of etiologically-relevant connectivity subtypes could improve diagnostic label accuracy in the non-syndromic ASD population and paves the way for personalized treatment approaches.

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