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@INPROCEEDINGS{DeKraker:1031458,
      author       = {DeKraker, Jordan and Cabalo, Donna Gift and Amunts, Katrin
                      and Rodriguez-Cruces, Raul and Evans, Alan C. and Valk,
                      Sofie},
      title        = {{H}ippo{M}aps: multiscale cartography of human hippocampal
                      organization},
      reportid     = {FZJ-2024-05677},
      year         = {2024},
      abstract     = {The hippocampus has a unique microarchitecture, is situated
                      at the nexus of multiple macroscale functional networks,
                      contributes to numerous cognitive and affective processes,
                      and is highly susceptible to brain pathology across common
                      disorders. The hippocampus can be understood and modeled as
                      a cortical (archicortical) structure with a 2D surface
                      topology [1]. Taking inspiration from neocortical
                      informatics tools like NeuroMaps [2], here, we introduce
                      HippoMaps, an open access toolbox and data warehouse for the
                      mapping and contextualization of hippocampal data on
                      hippocampal surfaces in the human brain.HippoMaps
                      capitalizes on a novel hippocampal unfolding approach as
                      well as shape intrinsic cross-subject and cross-modal
                      registration capabilities [3]. We initialize this repository
                      with data spanning 3D histology [4,5], structural MRI and
                      resting-state functional MRI (rsfMRI) obtained at 3 and 7
                      Tesla [6,7], as well as intracranial encephalography (iEEG)
                      recordings in epilepsy patients [8].We present 30 novel,
                      detailed maps of hippocampal structural and functional
                      features. Structural measures derived from quantitative MRI
                      and histology tend to show sharp subfield differentiation,
                      whereas functional measures such as rsfMRI and iEEG band
                      powers show gradual anterior-posterior differentiation. We
                      show how such maps can be related to one another using a
                      tailored approach for spatial map association that corrects
                      for autocorrelation. This provides a method for
                      contextualizing hippocampal data in future work. Code and
                      tools are compliant with community standards, and are
                      provided as comprehensive online tutorials that reproduce
                      the figures shown here.Bioinformatics data are not
                      inherently useful unless context is given, for example, by
                      their inter-relationships and their links to disease or
                      cognitive processes. Here we provide a common space and
                      toolbox for such comparisons in the hippocampus, spanning
                      methodologies and modalities, spatial scales, as well as
                      clinical and basic research contexts. Some maps have already
                      been generated and uploaded to HippoMaps by members of the
                      broader research community, and we further discourse in the
                      spirit of open and iterative scientific resource
                      development.<br><br>[1] DeKraker J, et al. Automated
                      hippocampal unfolding for morphometry and subfield
                      segmentation with HippUnfold. Elife. 2022;11.
                      doi:10.7554/eLife.77945<br>[2] Markello RD, et al.
                      neuromaps: structural and functional interpretation of brain
                      maps. Nat Methods. 2022;19: 1472–1479.<br>[3] DeKraker J,
                      et al. Evaluation of surface-based hippocampal registration
                      using ground-truth subfield definitions. Elife. 2023;12.
                      doi:10.7554/eLife.88404<br>[4] Amunts K, et al. BigBrain: an
                      ultrahigh-resolution 3D human brain model. Science.
                      2013;340: 1472–1475.<br>[5] Alkemade A, et al. A unified
                      3D map of microscopic architecture and MRI of the human
                      brain. Sci Adv. 2022;8: eabj7892.<br>[6] Royer J, et al. An
                      Open MRI Dataset For Multiscale Neuroscience. Sci Data.
                      2022;9: 569.<br>[7] Cabalo DG, et al. Multimodal precision
                      neuroimaging of the individual human brain at ultra-high
                      fields. bioRxiv. 2024. p. 2024.06.17.596303.
                      doi:10.1101/2024.06.17.596303<br>[8] Frauscher B, et al.
                      Atlas of the normal intracranial electroencephalogram:
                      neurophysiological awake activity in different cortical
                      areas. Brain. 2018;141: 1130–1144.},
      month         = {Sep},
      date          = {2024-09-09},
      organization  = {8th BigBrain Workshop, Padua (Italy),
                       9 Sep 2024 - 11 Sep 2024},
      subtyp        = {After Call},
      cin          = {INM-1},
      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)
                      / 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:(EU-Grant)101147319},
      typ          = {PUB:(DE-HGF)6},
      url          = {https://juser.fz-juelich.de/record/1031458},
}