Emergence of grid-like activity patterns during the formation of cognitive maps
Poster No:
829
Submission Type:
Abstract Submission
Authors:
Liangyue Song1, Joern Alexander Quent1, Yueting Su1, Xinyu Liang1, Deniz Vatansever1
Institutions:
1Fudan University, Shanghai, Shanghai
First Author:
Co-Author(s):
Introduction:
Grid-like brain activity patterns in the human enthorhinal cortex (EC) constitute a fundamental neurophysiological signature of cognitive maps – an internal represention of the relative positions of objects and landmarks in our environments [1]. While these patterns have been documented in trained participants [2,3], their developmental trajectory during the acquisition of novel spatial knowledge remains unknown. To address this gap, here we investigated the emergence of grid-like activity patterns during learning of a virtual reality environment, where participants engaged in an object location memory task. By analyzing grid orientation and amplitude dynamics, we provided further insights on the adaptive nature of navigational neural codes throughout spatial learning.
Methods:
A group of 55 participants (Mean age=23.7, SD = 2.21, F/M = 34/21) were scanned at 3T MRI (MB-BOLD, TR = 0.8 s, TE = 37 ms, 2 mm isotropic voxels), while performing two runs of an Object Location Memory (OLM) paradigm in virtual reality. In this task, participants were asked to learn the locations of six hidden objects through repeated trials. Imaging data was minimally preprocessed using the HCP pipelines [4] with MSMAll registeration to fsLR_32k cifti space [5]. Our grid analysis implemented a cross-validation approach using two sequential GLMs. Continuous movement events were divided into five folds, with four fold determining preferred grid orientation in the right EC (GLM1), and the remaining fold classifying events as aligned or misaligned relative to this orientation. Grid amplitude was calculated from the contrast between on-grid and off-grid events in GLM2. Statistical significance was estimated through non-parametric permutation testing using PALM (cFDR, q < .05).
Results:
Our analysis revealed converging findings at both behavioral and neural levels. Participants demonstrated significant improvement in placement accuracy between runs (p = 0.02), indicating successful spatial learning. At the neural level, we found significantly non-uniform distribution of preferred angles in the right EC for 45 participants across both runs (p<0.05), in which participants showing unstable grid orientations between runs (n = 25) exhibited significantly greater performance improvements (t = -2.26, p = 0.03). Moreover, six-fold modulation of fMRI signals extended beyond EC into visual and default mode network regions, and further investigation of grid-like activity patterns using on-grid versus off-grid event contrasts yielded significant results across multiple brain regions. We observed these patterns in bilateral EC as well as medial prefrontal (mPFC), orbitofrontal (OFC), and posterior parietal cortices (PPC). These findings align with previous grid analysis studies, supporting the robustness of our results. Importantly, the BOLD signal difference between on-grid and off-grid events showed significant enhancement in the second run (t = -2.27, p = 0.02), suggesting enhanced grid representations with the formation of spatial cognitive maps.
Conclusions:
Our research findings demonstrate the presence and evolution of grid-like activity patterns during spatial learning in a virtual reality environment. Unstable grid orientations were associated with greater performance improvements, possibly reflecting reorganization of neural representations with continuous learning. Additionally, the progressive increase in grid amplitude may also indicate strengthening of grid coding mechanisms during spatial learning. These findings illustrate the dynamic nature of grid-like representations during cognitive map formation, highlighting their potential role in optimizing spatial navigation and memory performance. Collectively, this work advances our understanding of how the human brain develops and refines its spatial representation systems during active learning.
Higher Cognitive Functions:
Space, Time and Number Coding 2
Learning and Memory:
Learning and Memory Other 1
Modeling and Analysis Methods:
Activation (eg. BOLD task-fMRI)
Keywords:
Cognition
Learning
1|2Indicates the priority used for review
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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.
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[2] Doeller, C.F. et al., (2010). Evidence for grid cells in a human memory network’, Nature, 463(7281), 657–661.
[3] Constantinescu A.O., O’Reilly J.X., & Behrens T.E. (2016). ‘Organizing conceptual knowledge in humans with a gridlike code’. Science, 352(6292), 1464–1468.
[4] Glasser M.F. et al. (2016), The Human Connectome Project's neuroimaging approach’, Nature Neuroscience, 19, 1175–1187.
[5] Ji J.L. et al. (2023) QuNex-An integrative platform for reproducible neuroimaging analytics. Frontiers in neuroinformatics, 17, 1104508.