The hippocampus is important for the learning of episodic and spatial memory, but how it coordinates its activity with other memory-related brain structures is not well known. Of particular interest is the prefrontal cortex (PFC), because of its diverse roles in attention, working memory, long-term memory storage and memory-guided decision making. The goal of this project is to investigate whether neural activity is systematically coordinated between hippocampus and PFC during memory-guided decision making.
Sharp wave ripples (SWR) are a prominent high-frequency hippocampal oscillation linked to memory-guided decision making that occur when an animal is immobile. During a SWR, neurons that were previously active for a particular spatial location in an environment "replay" their activity in a compressed sequence, as if the animal were currently moving through the environment. Interestingly, these compressed sequences of neural activity occur in both the forward and reverse direction -- that is, both following a path the animal might have taken through the environment (forward SWR) and retracing a path backwards (reverse SWR). Because these SWR events often occur at critical decision points, during the receiving of reward, or during sleep, they are thought to reflect planning of future actions based on past memories (memory recall) and/or consolidation of rewarded behaviors. Furthermore, reverse replay sequences are modulated by the rate of reward while forward replay are not (Ambrose et al. 2016), suggesting that reverse replay events are more involved in the consolidation of memories during learning whereas forward replays are more important for retrieval of past memories. If this is the case, then we might expect memory information to be transferred from PFC to hippocampus during forward replays -- because of the role of PFC in long term storage of memories -- and memory information to be transferred from hippocampus to PFC during reverse replays -- to consolidate memories during learning.
To test this hypothesis, neural activity was simultaneously recorded in PFC (pre-limbic and infralimbic) and hippocampus (CA1 and intermediate CA1) of three rats learning a W-track spatial alternation task (Figure 1a,c). In the task, the rats had to run to the opposite reward well at the end of each arm of the track (left and right arms) while returning to the center well in-between visits to each arm. We analyzed rhythmic local field potential activity occurring during sharp wave ripples in both hippocampus and PFC.
We are interested in answering the following questions:
This week I continued to work on getting the data saved on the cluster in a friendly format. In the past, saving results to the cluster has worked fine, but it has been difficult to aggregate the data from the results. The aggregation of the data has to be somewhat flexible, because we want to look at it over learning, between animals, and over different ripple replay categories.
I refactored the code to use a new python library xarray. Long story short, I think this new library is a better fit for creating the data structures needed for spectral connectivity measures. I tested saving this data structure to disk and made sure I could fetch the different results (these can vary by whether we are considering all the ripples or a subset of the ripples, or by the frequency resolution and time window of the multitaper parameters, and by connectivity measure). See this notebook for more details about xarray and the data structures.
After this, I created the new data structures for two different sessions:
And explored how to combine these data structures and average over brain area pairs (e.g. the average coherence magnitude from CA1 to PFC). The hope is this will make it easier once the code moves to the cluster, because the code will generalize from combining two sessions to an arbitrary number of sessions. See this notebook for further details.
Still trying to regenerate the plots to verify these conclusions from the results section:
Also still need to compare connectivity between replay types and over learning.
The code is almost there to re-run on the cluster, but there are still some bugs.
I was thinking about the full set of plots we need to do due diligence with regard to characterizing the ripple-triggered connectivity. I wrote up a checklist of plots that I think it makes sense to have here.
Any thoughts on what other plots for the connectivity analysis are needed?
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