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@ARTICLE{Nebli:1028714,
      author       = {Nebli, Ahmed and Schiffer, Christian and Niu, Meiqi and
                      Palomero-Gallagher, Nicola and Amunts, Katrin and
                      Dickscheid, Timo},
      title        = {{G}enerative {M}odelling of {C}ortical {R}eceptor
                      {D}istributions from {C}ytoarchitectonic {I}mages in the
                      {M}acaque {B}rain},
      journal      = {Neuroinformatics},
      volume       = {22},
      issn         = {1539-2791},
      address      = {New York, NY},
      publisher    = {Springer},
      reportid     = {FZJ-2024-04771},
      pages        = {389-402},
      year         = {2024},
      abstract     = {Neurotransmitter receptor densities are relevant for
                      understanding the molecular architecture of brain regions.
                      Quantitative in vitro receptor autoradiography, has been
                      introduced to map neurotransmitter receptor distributions of
                      brain areas. However, it is very time and cost-intensive,
                      which makes it challenging to obtain whole-brain
                      distributions. At the same time, high-throughput light
                      microscopy and 3D reconstructions have enabled
                      high-resolution brain maps capturing measures of cell
                      density across the whole human brain. Aiming to bridge gaps
                      in receptor measurements for building detailed whole-brain
                      atlases, we study the feasibility of predicting realistic
                      neurotransmitter density distributions from cell-body
                      stainings. Specifically, we utilize conditional Generative
                      Adversarial Networks (cGANs) to predict the density
                      distributions of the M2 receptor of acetylcholine and the
                      kainate receptor for glutamate in the macaque monkey’s
                      primary visual (V1) and motor cortex (M1), based on light
                      microscopic scans of cell-body stained sections. Our model
                      is trained on corresponding patches from aligned consecutive
                      sections that display cell-body and receptor distributions,
                      ensuring a mapping between the two modalities. Evaluations
                      of our cGANs, both qualitative and quantitative, show their
                      capability to predict receptor densities from cell-body
                      stained sections while maintaining cortical features such as
                      laminar thickness and curvature. Our work underscores the
                      feasibility of cross-modality image translation problems to
                      address data gaps in multi-modal brain atlases.},
      cin          = {INM-1},
      ddc          = {540},
      cid          = {I:(DE-Juel1)INM-1-20090406},
      pnm          = {5251 - Multilevel Brain Organization and Variability
                      (POF4-525) / 5254 - Neuroscientific Data Analytics and AI
                      (POF4-525) / HIBALL - Helmholtz International BigBrain
                      Analytics and Learning Laboratory (HIBALL) (InterLabs-0015)
                      / Helmholtz AI - Helmholtz Artificial Intelligence
                      Coordination Unit – Local Unit FZJ (E.40401.62) / HBP SGA3
                      - Human Brain Project Specific Grant Agreement 3 (945539) /
                      EBRAINS 2.0 - EBRAINS 2.0: A Research Infrastructure to
                      Advance Neuroscience and Brain Health (101147319)},
      pid          = {G:(DE-HGF)POF4-5251 / G:(DE-HGF)POF4-5254 /
                      G:(DE-HGF)InterLabs-0015 / G:(DE-Juel-1)E.40401.62 /
                      G:(EU-Grant)945539 / G:(EU-Grant)101147319},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {38976151},
      UT           = {WOS:001264639700002},
      doi          = {10.1007/s12021-024-09673-7},
      url          = {https://juser.fz-juelich.de/record/1028714},
}