Developmental Landscape of Gradients in Human Hippocampus

  1. Beijing Advanced Innovation Center for Biomedical Engineering, School of Biological Science & Medical Engineering, Beihang University, Beijing, China
  2. State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
  3. Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China
  4. Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, Fudan University, Ministry of Education, Shanghai, China
  5. Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, China
  6. IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
  7. State Key Laboratory of Virtual Reaility technology and Systems, Beihang University, Beijing, China
  8. Child Mind Institute, New York, NY, United States
  9. School of Systems Science, Beijing Normal University, Beijing, China
  10. Center for MRI Research, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, China
  11. Beijing City Key Laboratory for Medical Physics and Engineering, Institute of Heavy Ion Physics, School of Physics, Peking University, Beijing, China
  12. Beijing Huilongguan Hospital, Peking University Huilongguan Clinical Medical School, Beijing, China
  13. IDG/McGovern Institute for Brain Research, Peking University, Beijing, China
  14. Chinese Institute for Brain Research, Beijing, China
  15. National Basic Science Data Center, Beijing, China
  16. Developmental Population Neuroscience Research Center, Beijing Normal University, Beijing, China

Peer review process

Not revised: This Reviewed Preprint includes the authors’ original preprint (without revision), an eLife assessment, and public reviews.

Read more about eLife’s peer review process.

Editors

  • Reviewing Editor
    Alex Fornito
    Monash University, Clayton, Australia
  • Senior Editor
    Jonathan Roiser
    University College London, London, United Kingdom

Reviewer #1 (Public review):

Summary:

The authors studied the development of hippocampal connectivity gradients based on open datasets and performed correlation analyses with other MRI features as well as gene expression information from other datasets. Although the main findings are correlational and cross-sectional, the analyses are overall sophisticated and replicated in several datasets.

Strengths:

The hippocampus is a key region in understanding large-scale brain organization and cognition, and the authors applied advanced and suitable analytics to study its development. The paper is overall well-organized and well-written, and the findings are relevant for studying large-scale brain development.

Weaknesses:

While sophisticated, several of the analyses appear mainly correlational, cross-sectional, and rely on cross-dataset contextualization, which should also be stated as a limitation of the current work.

Reviewer #2 (Public review):

Summary:

In this manuscript, the authors aim to assess how the functional organisation of the hippocampus is related to the geometry and neurobiological differences of the hippocampus. In particular, the authors focus on the first three eigenvectors of hippocampal-cortical functional connectivity, based on non-linear dimensionality reduction on resting-state functional MRI data. Furthermore, the work aims to describe changes in these functional axes and their relation to other factors throughout youth and evaluate whether they are predictive of individual variations in cognition.

Strengths:

A major strength of this study is the attempt to replicate key findings across multiple developmental cohorts.

Weaknesses:

The major weaknesses of the manuscript center on gaps in technical transparency and several conceptual inaccuracies. The machine learning methodology used for cognitive prediction is scarce, leaving little means to evaluate whether the behavioral results suffer from data leakage or overfitting. The introduction sets up an oversimplified historical premise regarding the field's understanding and appreciation of hippocampal connectivity, and contains several incorrect references that throw doubt on the argumentation. Additionally, T1w/T2w signal intensity is incorrectly used as synonymous with myelin, despite gold-standard histological validation showing a non-significant correlation between T1w/T2w and myelin staining (Sandrone et al., 2013).

Appraisal of Aims and Conclusions:

The authors partially achieve their aims by illustrating certain age-related changes in hippocampal function; however, the correlative study design is not equipped to examine how these changes are "shaped" by geometry, myelination, or gene expression (especially the latter two). Furthermore, conclusions were often overstated based on small effect sizes.

Context and Field Impact:

This work adds to a growing body of literature focused on gradient-based representations of hippocampal topology. By applying these methods across a wide developmental age bracket, it provides a useful reference point for how the hippocampus and wider cortex interact during maturation. To improve utility to the neuroimaging and cognitive neuroscience communities, the nesting of subfields within the eigenvector topology should be addressed, too.

  1. Howard Hughes Medical Institute
  2. Wellcome Trust
  3. Max-Planck-Gesellschaft
  4. Knut and Alice Wallenberg Foundation