Our method relies on several combined technologies. Surgical methods were developed to implant a hippocampal window that allowed for chronic sub-cellular resolution imaging within the CA1 region of the hippocampus in behaving mice. The combination of this window with a custom two-photon microscope and background light suppression methods allowed for imaging in mice interacting with a recently described visual virtual reality system. Using the recently developed genetically encoded calcium indicator GCaMP3, our methods allowed for the study of spatially modulated activity patterns of pyramidal neurons in stratum pyramidale, putative interneurons in stratum oriens, and apical dendrites in stratum radiatum. By imaging the activity of populations of ~80–100 neurons in stratum pyramidale in mice trained to navigate along a virtual linear track, place cells were identified that had characteristics very similar to spiking rate-defined place cells in both real and virtual environments15,26,27,37
Tetrodes are currently the most common method used to record CA1 place cell activity in behaving mice. Tetrode array recordings combined with spike sorting procedures can be used to identify many (~5) single units per tetrode38,39
. While sub-millisecond temporal resolution is a major advantage of this method compared to imaging, the current limitations of tetrode methods are the low spatial resolution (>100s of microns) and sparse sampling within the micro-circuitry38,40
Our imaging method has several advantages over tetrode methods. For example, it is possible to report the precise anatomical position of functionally identified neurons within the micro-circuitry. Imaging also allows for functional recording from sub-cellular compartments, identifying all neurons (even silent cells), and can take advantage of the growing number of available genetic tools41
. It should be possible to image subcellular dynamics such as signal transduction42
and structural plasticity43
in the context of learning and memory during behavioral paradigms. Additionally, optical methods may allow for identifying the functional properties of specific neurons in a large population, followed by either the subsequent reconstruction of the underlying connections44
or modification of their activity45
. Finally, imaging methods can allow for the unambiguous identification and recording of the same neurons over many weeks. Although this was not the focus of this research, to address the technical feasibility of such studies we have imaged the same region of CA1 over multiple days (see Supplemental Fig. 5
Here it was assumed that calcium transients could be used as a proxy for spiking activity. Sodium action potentials have been shown to generate calcium transients in GCaMP-3 expressing hippocampal pyramidal neurons in brain slices. Calcium transient amplitude was found to increase linearly with the number of evoked action potentials, until saturation at >~10 action potentials13
. A quantitatively similar relationship between spiking activity and calcium transient amplitude was found for neocortical neurons in brain slice and in vivo in both anesthetized and awake mice13
. It is therefore reasonable to assume that the spiking activity of the CA1 neurons in vivo generated calcium transients similar to what was observed previously in slices13
. Using these previous calcium transient measurements13
and our extracellular place cell recordings, we calculated the spike induced calcium transients that would be expected in our imaging experiments (Supplemental Fig. 3
). The characteristics of the optically defined place fields are consistent with and can be fully explained by calcium transients induced by sodium action potentials. Therefore, the contribution of other sources to our calcium transients, such as calcium influx due to synaptic input or dendritic calcium spikes, is likely small.
It is unlikely that our methods allow for detecting single action potentials13
, determining firing rates or reliably counting the number of spikes. While these limitations likely do not pose a problem for identifying place fields in place cells due to the dramatic increase in spiking rate in the place field and the ability to average over many traversals, it is still possible that place cells with low activity levels may not be detected.
Cortical excavation was used to expose the hippocampus for acute, anesthetized cat electrophysiology experiments nearly 50 years ago46
. More recently, cortical excavation was combined with a polystyrene tube filled with agarose and sealed with a coverslip to facilitate two-photon imaging of dendritic spines in an acute, anesthetized mouse preparation14
. Here, these methods were used as a starting point to develop a chronic hippocampal window designed for imaging in behaving mice. The stainless steel cannula and coverslip directly bonded to the external capsule surface with Kwik-sil formed a rigid support structure that minimized brain motion during animal movements and allowed for repeated imaging of the hippocampus for weeks. We found that removing the cortex overlying the hippocampus did not detectably alter the mouse's performance of the task, or the hippocampal dynamics and place cell properties that were measured in our preparation compared to control mice.
Miniaturized head-mounted microscopes47,48
may allow for hippocampal imaging in freely moving animals. Such experiments would benefit from the natural array of inputs, as opposed to the lack of vestibular input and potentially altered gait of our mice. Our methods, however, have the advantages of not requiring a miniaturized microscope, easy combination with electrophysiology, and the potential to manipulate specific environmental cues using virtual reality in ways that would be difficult or impossible in real environments.
We used an ICA/PCA algorithm to identify individual neurons based on both their spatial and temporal characteristics21
. This method, however, could not identify silent neurons and occasionally missed active cells, making the estimation of the number of potentially active neurons difficult. Therefore, it was difficult to quantify the exact fraction of neurons that were place cells. Based simply on the morphology of neurons in the fields of view, we estimate that our fields contained ~80–100 neurons, meaning that ~15–20% of the neurons were place cells, slightly less than previous estimates15
. In addition to the uncertain number of potentially active neurons, this slight difference could also be due to differences in the recording methods and differences in the definitions of place fields.
The ICA/PCA algorithm was successful in limiting the crosstalk between neighboring ROIs. However, when studying the spatio-temporal organization of neurons in CA1, the possibility of residual crosstalk, residual brain motion or a common neuropil signal between the most closely neighboring cells (<~35 microns) could not be ruled out. This is one possible explanation for the statistically outlying data points in corresponding to the most closely neighboring cells. An alternative explanation for these outlying data points is that they represent small clusters of functionally similar neurons, as recent studies have suggested5,9
For place cells that were further separated (>~35 microns), the distance between their place fields along the track was statistically unrelated to the distance between their positions in the hippocampus. A few previous electrode recording studies found anatomically organized clusters of functionally similar hippocampal neurons on the 0.6–1mm spatial scale6,7
, while a separate study measuring CA1 place cell location concluded that there was a lack of anatomical spatial organization8
. Because of the low spatial resolution and sparse sampling within micro-circuitry38
, these previous methods provided only an indirect measure of the spatial organization of place cells in CA1. In contrast, using our methods, we were able to directly measure the spatial organization. While possible crosstalk concerns did not allow us to unambiguously measure the organization down to the finest scale, it was possible to conclude that place cells are anatomically distributed down to a scale of at least ~35 microns.
For place cells separated by at least this scale, there was also no relationship between the correlation between their temporal activity patterns and the physical distance between them. Interestingly, for all neurons, a statistically significant decrease in temporal correlation was found between the neurons the further they were from each other in the physical space of the hippocampus. It is interesting to note that while this decrease is significant, both the overall level of correlation and the rate of correlation decrease as a function of distance are nearly an order of magnitude smaller than has been observed in the motor cortex of behaving mice11
Finally, we note that spatio-temporal organization can occur in many forms, most of which were not examined here. The methods outlined in this research should allow for future studies to search more thoroughly for micro-organization within the hippocampus.