P327 Deciphering the Dynamics of Memory Encoding and Recall in the Hippocampus Using Information Theory and Graph Theory
Jess Yu*1, Hardik Rajpal2, Mary Ann Go1, Simon Schultz1
1Department of Bioengineering and Centre for Neurotechnology, Imperial College London, United Kingdom, SW7 2AZ 2Department of Mathematics and Centre for Complexity Science, Imperial College London, United Kingdom, SW7 2AZ
*Email: jin.yu21@imperial.ac.uk
Introduction Alzheimer's disease (AD) profoundly impairs spatial navigation, a critical cognitive function dependent on hippocampal processing. While previous studies have documented the deterioration of place cell activity in AD, the mechanisms by which AD disrupts information processing across neural populations remain not fully understood. Traditional analyses focusing on individual neurons fail to capture the collective properties of neural circuits. We hypothesized that AD pathology disrupts not only individual cellular encoding but also the integration and sharing of spatial information across functional neuronal assemblies, leading to compromised spatial navigation. Methods We analysed hippocampal CA1 neural recordings from two-photon calcium imaging from AD and wild-type (WT) mice, both young and old, during spatial navigation tasks in familiar environments and novel environments. At the single-cell level, we quantified spatial information using mutual information (MI) between neural spikes and location, and partial information decomposition (PID) [1] for the pair of neurons and location. For population-level analysis, we constructed functional networks using pairwise MI, identified stable functional neuronal assemblies using Markov Stability detection [2], and applied PID to quantify how assemblies collectively encode spatial information through redundancy, synergy, joint mutual information, and redundancy-Synergy Index. Results Our analysis revealed multi-scale disruption of spatial information processing in AD. At the single-cell level, AD-Old (ADO) mice showed significantly reduced spatially informative neurons and lower spatial information content. At the assembly level, we uncovered profound deficits in information integration. ADO assemblies showed significantly reduced redundancy and synergy compared to WT-Young controls, indicating impaired information sharing. The Redundancy-Synergy Index revealed a significant shift in the balance between redundant and synergistic processing across neural assemblies. Discussion These findings provide novel insights into how AD disrupts neural information processing across multiple scales. The parallel degradation of both cellular encoding and assembly-level information integration suggests a compound effect of AD pathology on spatial navigation circuits. The reduced information sharing between assemblies points to a breakdown in coordinated activity necessary for effective spatial navigation. This multi-scale information-theoretic approach reveals that AD impairs not just individual neural responses but the mechanisms by which neural assemblies integrate spatial information, potentially guiding development of assembly-level therapeutic strategies.
Acknowledgements This work was supported by the Engineering and Physical Sciences Research Council (EPSRC) through the Physics of Life grant [EP/W024020/1]. References [1] Williams, P. L., & Beer, R. D. (2010). Nonnegative decomposition of multivariate information. arXiv preprint arXiv:1004.2515. https://doi.org/10.48550/arXiv.1004.2515 [2] Delvenne, J.-C., Yaliraki, S. N., & Barahona, M. (2008). Stability of graph communities across time scales. arXiv preprint arXiv:0812.1811. https://doi.org/10.48550/arXiv.0812.1811
