Hippocampal Network Interactions Reveal a Dual-process Framework of Human Navigation
Presented During:
Thursday, June 26, 2025: 11:30 AM - 12:45 PM
Brisbane Convention & Exhibition Centre
Room:
M3 (Mezzanine Level)
Poster No:
1213
Submission Type:
Abstract Submission
Authors:
Zhili Li1, Kaixiang Zhuang1, Xinyu Liang1, Joern Alexander Quent1, Liangyue Song1, Yueting Su1, Deniz Vatansever1
Institutions:
1Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, ShangHai, China
First Author:
Zhili Li
Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University
ShangHai, China
Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University
ShangHai, China
Co-Author(s):
Kaixiang Zhuang
Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University
ShangHai, China
Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University
ShangHai, China
Xinyu Liang
Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University
ShangHai, China
Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University
ShangHai, China
Joern Alexander Quent
Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University
ShangHai, China
Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University
ShangHai, China
Liangyue Song
Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University
ShangHai, China
Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University
ShangHai, China
Yueting Su
Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University
ShangHai, China
Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University
ShangHai, China
Deniz Vatansever
Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University
ShangHai, China
Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University
ShangHai, China
Introduction:
Navigational abilities are fundamental to human survival and daily functioning, enabling us to explore new environments, find resources, and return home safely. In the real world, navigational strategies are suggested to rely on a combination of internal memory representations (e.g. cognitive maps) and external visual cues (e.g. landmarks). However, the neural substrates of these cognitive strategies remain unclear [1]. Through a machine learning-based dimensionality reduction approach with robust cross-validation strategies [2], we first identified two core processes underlying navigational skill: memory-based and map-based navigation. In a large cohort, we then investigated the neural basis of these cognitive processes by examining intrinsic functional interactions of the anterior hippocampus – a key region implicated in human spatial navigation.
Methods:
Using high-quality HCP-style data acquisition procedures, we obtained resting-state fMRI data (AP/PA) from 125 participants (mean = 25.39 years, SD = 2.97, F/M ratio = 84/41) at 3T MRI scanner (TR = 0.8 s, TE = 37, voxel size = 2 mm isotropic, 976 volumes). Data preprocessing followed HCP pipelines, including MSMAll registration to the fsLR_32k cifti space. Responses to the Santa Barbara Sense of Direction (SBSOD) scale [4] were combined with an independent dataset [5], and dimensionality reduction was performed using orthogonal projective non-negative matrix factorization (OPNMF), optimized with cross-validation to ensure robust and generalizable decomposition. Component scores from the top two principal components were used as regressors in a standard general linear model (GLM) to analyse their association with anterior hippocampal functional connectivity. Non-parametric permutation testing with PALM was used for statistical analysis, controlling for multiple comparisons (cFDR, q < 0.05).
Results:
Dimensionality reduction of self-reported responses to the SBSOD scale revealed two robust components: Component 1 (memory-based navigation, e.g., "I have a good mental map") and Component 2 (map-based navigation, e.g., "I am good at reading maps"). Component 1 scores were positively associated with anterior hippocampal connectivity to regions within the default mode network (DMN), including the medial prefrontal and posterior cingulate cortices. This supports the role of hippocampal-DMN interactions in memory-based navigation, facilitating cognitive map construction and mental simulation. Component 2 scores correlated with anterior hippocampal connectivity to the dorsal and ventral visual streams, including the parietal cortex and fusiform gyrus. These results underscore the reliance of map-based navigation on visual processing and the integration of external spatial cues.
·Robust decomposition of navigational skills reveals two distinct cognitive processes
·Associations between navigational strategies and anterior hippocampal intrinsic functional connectivity
Conclusions:
By combining advanced dimensionality reduction techniques and robust statistical analysis, we revealed distinct hippocampal network dynamics underpinning memory-based and map-based navigation strategies. Memory-based navigation engages the hippocampus-DMN connectivity axis to support internalized spatial representations, while map-based navigation recruits hippocampal-visual stream connectivity for perceptual processing. These findings highlight the anterior hippocampus as a dynamic hub linking mnemonic and perceptual systems and contribute to a dual-process framework of human navigation. Future work will explore the impact of these patterns on navigational task performance and real-world behaviour.
Higher Cognitive Functions:
Space, Time and Number Coding 2
Modeling and Analysis Methods:
Connectivity (eg. functional, effective, structural) 1
fMRI Connectivity and Network Modeling
Task-Independent and Resting-State Analysis
Novel Imaging Acquisition Methods:
BOLD fMRI
Keywords:
Cognition
FUNCTIONAL MRI
Other - Spatial Navigation
1|2Indicates the priority used for review
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[2] Chen, J. et al. (2020). Neurobiological Divergence of the Positive and Negative Schizophrenia Subtypes Identified on a New Factor Structure of Psychopathology Using Non-negative Factorization: An International Machine Learning Study. Biological psychiatry, vol. 87, no. 3, pp. 282–293.
[3] Glasser M. et al., (2016), ‘The Human Connectome Project's neuroimaging approach’, Nature Neuroscience, vol. 19, pp. 1175–1187
[4] Hegarty, M., Richardson, A. E., Montello, D. R., Lovelace, K., & Subbiah, I. (2002). Development of a self-report measure of environmental spatial ability. Intelligence, vol. 30, no. 5, pp. 425–448.
[5] Clark, I. A., & Maguire, E. A. (2023). Release of cognitive and multimodal MRI data including real-world tasks and hippocampal subfield segmentations. Scientific data, vol. 10, no. 1, pp. 540.