Seigfred Prado,
Simon R Schultz,
Mary Ann GoNeurostars topic for Q&AAlthough much is known about neural circuits and molecular pathways required for normal hippocampal functions, the processes by which neurodegenerative diseases, such as Alzheimer’s Disease (AD), disable the functioning of the hippocampus and connected structures remain to be determined. In order to make substantial advances in the treatment of such diseases, we must improve our understanding of how neural circuits process information and how they are disrupted during the progression of these diseases. Recent advances in optical imaging technologies that allow simultaneous recording of large populations of neurons in deeper structures [1] have shown great promise for revealing circuit dynamics during memory tasks [2]. However, to date, no study has revealed how large numbers of neurons in hippocampal-cortical circuits act together to encode, store and retrieve memories in animal models of AD. In this work, we explored the use of neural manifold analysis techniques to characterising brain circuit dynamics in neurodegenerative disease. To understand more precisely the basis of memory and cognitive impairments in AD, we extracted the underlying neural manifolds in large-scale neural responses of hippocampal circuits involved in spatial cognition of behaving mice. For validation, we simulated a model that generates a set of data that mimics the neural activity of hippocampal cells of mouse models running on a linear circular track, while taking into account the effects of amyloid-beta plaques on circuit dynamics [3]. We compare our model with real data obtained by multiphoton imaging of hippocampal CA1 cells in mice engaged in a spatial memory task. We used recurrence analysis to show how neural manifolds evolve over time during memory encoding, storage and recall processes in a repetitive memory task. This work will help with understanding how amyloid-beta proteins affect the neural manifolds for spatial memory, which is particularly disturbed during AD.
