HippoMaps: multiscale cartography of human hippocampal organization
Poster No:
1491
Submission Type:
Abstract Submission
Authors:
Jordan DeKraker1, Alexander Ngo2, Donna Gift Cabalo2, Alan Evans2, Boris Bernhardt2
Institutions:
1McGill University, Montreal, QC, 2McGill University, Montreal, Quebec
First Author:
Co-Author(s):
Introduction:
The hippocampus has a unique microarchitecture, is situated at the nexus of multiple macroscale functional networks, contributes to numerous cognitive as well as affective processes, and is highly susceptible to brain pathology across common disorders. These features make the hippocampus a model to understand how brain structure covaries with function, in both health and disease. Here, we introduce HippoMaps, an open access toolbox and online data warehouse for the mapping and contextualization of subregional hippocampal data in the human brain.
Methods:
HippoMaps capitalizes on a unified hippocampal unfolding approach, HippUnfold (DeKraker et al. 2022), as well as shape intrinsic registration capabilities (DeKraker et al. 2023) to allow for cross-subject and cross-modal data aggregation across multiple scales. We initialize this repository with an unprecedented combination of hippocampal data spanning 3D ex-vivo histology, ex-vivo 9.4 Tesla MRI, as well as in-vivo structural MRI and resting-state functional MRI (rsfMRI) obtained at 3 and 7 Tesla, together with intracranial encephalography (iEEG) recordings in epilepsy patients. HippoMaps implements a range of non-parametric statistical tests to evaluate the similarity of standardized surface maps, while controlling for spatial autocorrelation within the hippocampal sheet-like topology. This provides a statistical foundation for accurate enrichment analysis in the hippocampus (Alexander-Bloch et al. 2018; Karat et al. 2023; Vos de Wael et al. 2020). To facilitate broad adoption and continued development, we made scripts (http://github.io/MICA-MNI/hippomaps) and associated data (https://osf.io/92p34/) openly available, and provide expandable online tutorials and guidelines (http://hippomaps.readthedocs.io).
·Figure 1. Overview of Methods
Results:
We illustrate how the maps provided here, as well as extended data uploaded by the greater neuroimaging community, can reveal new principles of hippocampal organization. Here, we focus on the alignment of maps to two candidate divisions of the hippocampus: discrete subfields (Olsen et al. 2019) and a continuous geodesic anterior-posterior axis (Strange et al. 2014). We show that structural, or microcircuit organization, typically follows the subfield organization of the hippocampus, while functional fMRI connectivity at a macroscale and iEEG band powers tend to differ across the anterior-posterior axis of the hippocampus.
Conclusions:
By a normative reference for hippocampal subregional organization, the HippoMaps repository can be used to contextualize new data in future work. That is, a new map can be compared to this existing database to determine its structural and functional significance or its validity as a proxy measurement. In addition, the tools and tutorials provided by HippoMaps provide best-practices and statistically rigorous methods for structuring data storage and statistical analyses. Applications of this work span methodologies and modalities, spatial scales, as well as clinical and basic research contexts, and we encourage community feedback and contributions in the spirit of open and iterative scientific resource development.
Learning and Memory:
Long-Term Memory (Episodic and Semantic) 2
Modeling and Analysis Methods:
Image Registration and Computational Anatomy 1
Neuroanatomy, Physiology, Metabolism and Neurotransmission:
Anatomy and Functional Systems
Neuroinformatics and Data Sharing:
Databasing and Data Sharing
Novel Imaging Acquisition Methods:
Multi-Modal Imaging
Keywords:
Computational Neuroscience
Data Organization
Learning
Limbic Systems
Memory
Open Data
Open-Source Software
1|2Indicates the priority used for review
Abstract Information
By submitting your proposal, you grant permission for the Organization for Human Brain Mapping (OHBM) to distribute your work in any format, including video, audio print and electronic text through OHBM OnDemand, social media channels, the OHBM website, or other electronic publications and media.
The Open Science Special Interest Group (OSSIG) is introducing a reproducibility challenge for OHBM 2025. This new initiative aims to enhance the reproducibility of scientific results and foster collaborations between labs. Teams will consist of a “source” party and a “reproducing” party, and will be evaluated on the success of their replication, the openness of the source work, and additional deliverables. Click here for more information. Propose your OHBM abstract(s) as source work for future OHBM meetings by selecting one of the following options:
Please indicate below if your study was a "resting state" or "task-activation” study.
Healthy subjects only or patients (note that patient studies may also involve healthy subjects):
Was this research conducted in the United States?
Were any human subjects research approved by the relevant Institutional Review Board or ethics panel? NOTE: Any human subjects studies without IRB approval will be automatically rejected.
Were any animal research approved by the relevant IACUC or other animal research panel? NOTE: Any animal studies without IACUC approval will be automatically rejected.
Please indicate which methods were used in your research:
For human MRI, what field strength scanner do you use?
Which processing packages did you use for your study?
Provide references using APA citation style.
DeKraker, Jordan, Roy A. M. Haast, Mohamed D. Yousif, Bradley Karat, Jonathan C. Lau, Stefan Köhler, and Ali R. Khan. 2022. “Automated Hippocampal Unfolding for Morphometry and Subfield Segmentation with HippUnfold.” eLife 11 (December). https://doi.org/10.7554/eLife.77945.
DeKraker, Jordan, Nicola Palomero-Gallagher, Olga Kedo, Neda Ladbon-Bernasconi, Sascha E. A. Muenzing, Markus Axer, Katrin Amunts, Ali R. Khan, Boris C. Bernhardt, and Alan C. Evans. 2023. “Evaluation of Surface-Based Hippocampal Registration Using Ground-Truth Subfield Definitions.” eLife 12 (November). https://doi.org/10.7554/eLife.88404.
Karat, Bradley G., Jordan DeKraker, Uzair Hussain, Stefan Köhler, and Ali R. Khan. 2023. “Mapping the Macrostructure and Microstructure of the in Vivo Human Hippocampus Using Diffusion MRI.” Human Brain Mapping 44 (16): 5485–5503.
Olsen, Rosanna K., Valerie A. Carr, Ana M. Daugherty, Renaud La Joie, Robert S. C. Amaral, Katrin Amunts, Jean C. Augustinack, et al. 2019. “Progress Update from the Hippocampal Subfields Group.” Alzheimer’s & Dementia: The Journal of the Alzheimer's Association 11 (December):439–49.
Strange, Bryan A., Menno P. Witter, Ed S. Lein, and Edvard I. Moser. 2014. “Functional Organization of the Hippocampal Longitudinal Axis.” Nature Reviews. Neuroscience 15 (10): 655–69.
Vos de Wael, Reinder, Oualid Benkarim, Casey Paquola, Sara Lariviere, Jessica Royer, Shahin Tavakol, Ting Xu, et al. 2020. “BrainSpace: A Toolbox for the Analysis of Macroscale Gradients in Neuroimaging and Connectomics Datasets.” Communications Biology 3 (1): 103.