001031458 001__ 1031458
001031458 005__ 20241107210038.0
001031458 037__ $$aFZJ-2024-05677
001031458 041__ $$aEnglish
001031458 1001_ $$0P:(DE-HGF)0$$aDeKraker, Jordan$$b0$$eCorresponding author
001031458 1112_ $$a8th BigBrain Workshop$$cPadua$$d2024-09-09 - 2024-09-11$$wItaly
001031458 245__ $$aHippoMaps: multiscale cartography of human hippocampal organization
001031458 260__ $$c2024
001031458 3367_ $$033$$2EndNote$$aConference Paper
001031458 3367_ $$2DataCite$$aOther
001031458 3367_ $$2BibTeX$$aINPROCEEDINGS
001031458 3367_ $$2DRIVER$$aconferenceObject
001031458 3367_ $$2ORCID$$aLECTURE_SPEECH
001031458 3367_ $$0PUB:(DE-HGF)6$$2PUB:(DE-HGF)$$aConference Presentation$$bconf$$mconf$$s1730972441_23159$$xAfter Call
001031458 520__ $$aThe 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.
001031458 536__ $$0G:(DE-HGF)POF4-5251$$a5251 - Multilevel Brain Organization and Variability (POF4-525)$$cPOF4-525$$fPOF IV$$x0
001031458 536__ $$0G:(DE-HGF)POF4-5254$$a5254 - Neuroscientific Data Analytics and AI (POF4-525)$$cPOF4-525$$fPOF IV$$x1
001031458 536__ $$0G:(DE-HGF)InterLabs-0015$$aHIBALL - Helmholtz International BigBrain Analytics and Learning Laboratory (HIBALL) (InterLabs-0015)$$cInterLabs-0015$$x2
001031458 536__ $$0G:(EU-Grant)101147319$$aEBRAINS 2.0 - EBRAINS 2.0: A Research Infrastructure to Advance Neuroscience and Brain Health (101147319)$$c101147319$$fHORIZON-INFRA-2022-SERV-B-01$$x3
001031458 7001_ $$0P:(DE-HGF)0$$aCabalo, Donna Gift$$b1
001031458 7001_ $$0P:(DE-Juel1)131631$$aAmunts, Katrin$$b2$$ufzj
001031458 7001_ $$0P:(DE-HGF)0$$aRodriguez-Cruces, Raul$$b3
001031458 7001_ $$0P:(DE-HGF)0$$aEvans, Alan C.$$b4
001031458 7001_ $$0P:(DE-Juel1)173843$$aValk, Sofie$$b5$$ufzj
001031458 8564_ $$uhttps://events.hifis.net/event/1416/contributions/11280/
001031458 909CO $$ooai:juser.fz-juelich.de:1031458$$popenaire$$pVDB$$pec_fundedresources
001031458 9101_ $$0I:(DE-HGF)0$$6P:(DE-HGF)0$$a McGill University, Montreal$$b1
001031458 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)131631$$aForschungszentrum Jülich$$b2$$kFZJ
001031458 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)173843$$aForschungszentrum Jülich$$b5$$kFZJ
001031458 9131_ $$0G:(DE-HGF)POF4-525$$1G:(DE-HGF)POF4-520$$2G:(DE-HGF)POF4-500$$3G:(DE-HGF)POF4$$4G:(DE-HGF)POF$$9G:(DE-HGF)POF4-5251$$aDE-HGF$$bKey Technologies$$lNatural, Artificial and Cognitive Information Processing$$vDecoding Brain Organization and Dysfunction$$x0
001031458 9131_ $$0G:(DE-HGF)POF4-525$$1G:(DE-HGF)POF4-520$$2G:(DE-HGF)POF4-500$$3G:(DE-HGF)POF4$$4G:(DE-HGF)POF$$9G:(DE-HGF)POF4-5254$$aDE-HGF$$bKey Technologies$$lNatural, Artificial and Cognitive Information Processing$$vDecoding Brain Organization and Dysfunction$$x1
001031458 9141_ $$y2024
001031458 920__ $$lyes
001031458 9201_ $$0I:(DE-Juel1)INM-1-20090406$$kINM-1$$lStrukturelle und funktionelle Organisation des Gehirns$$x0
001031458 980__ $$aconf
001031458 980__ $$aVDB
001031458 980__ $$aI:(DE-Juel1)INM-1-20090406
001031458 980__ $$aUNRESTRICTED