Experience-dependent trends in CA1 theta and slow gamma rhythms in freely behaving mice

Abstract

CA1 place cells become more anticipatory with experience, an effect thought to be caused by NMDA receptor-dependent plasticity in the CA3-CA1 network. Theta (~5-12 Hz), slow gamma (~25-50 Hz), and fast gamma (~50-100 Hz) rhythms are thought to route spatial information in the hippocampal formation and to coordinate place cell ensembles. Yet, it is unknown whether these rhythms exhibit experience-dependent changes concurrent with those observed in place cells. Slow gamma rhythms are thought to indicate inputs from CA3 to CA1, and such inputs are thought to be strengthened with experience. Thus, we hypothesized that slow gamma rhythms would become more evident with experience. We tested this hypothesis using mice freely traversing a familiar circular track for three 10-min sessions per day. We found that slow gamma amplitude was reduced in the early minutes of the first session of each day, even though both theta and fast gamma amplitudes were elevated during this same period. However, in the first minutes of the second and third sessions of each day, all three rhythms were elevated. Interestingly, theta was elevated to a greater degree in the first minutes of the first session than in the first minutes of later sessions. Additionally, all three rhythms were strongly influenced by running speed in dynamic ways, with the influence of running speed on theta and slow gamma changing over time within and across sessions. These results raise the possibility that experience-dependent changes in hippocampal rhythms relate to changes in place cell activity that emerge with experience. NEW & NOTEWORTHY We show that CA1 theta, slow gamma, and fast gamma rhythms exhibit characteristic changes over time within sessions in familiar environments. These effects in familiar environments evolve across repeated sessions.

Keywords: gamma rhythms; hippocampus; mice; place cells; running speed; theta rhythms.

Fig. 1.

CA1 place field properties vary with experience. A: histological section showing example tetrode sites in CA1 cell body layer. Tracks of two individual tetrodes can be seen and are circled in black. B: example spike raster for a single place cell showing the locations of spikes (red dots) across multiple laps around a circular track in sessions 1–3. Fewer spikes were emitted on the first two laps of session 1 after which the firing rate increased while the place field expanded backward. No such effect is evident in sessions 2–3. The arrow next to the y-axis denotes the direction of movement. C: experiential changes in place field properties [top-to-bottom: center of mass (COM), field peak, first spike, last spike, normalized field size, normalized firing rate, normalized field width, skewness, firing rate asymmetry index (FRAI)] across time for sessions 1–3 (left to right) for all included place cells. D: differences between the curves shown in C. Left column shows the session 2 curves subtracted from the session 1 curves, while the middle and right columns show the session 3 curves subtracted from the session 1 and 2 curves, respectively. Gray shaded areas denote 95% simultaneous confidence bands. Corresponding pointwise level α < 0.01 for all bands.

Fig. 2.

AR1 correction of residuals in base model. Left: autocorrelation functions of the residuals for each frequency fit under the base model (see text). Middle: corrected residuals for the same model assuming an AR1 error process. Right: difference in the magnitude of autocorrelation between the two models, where purple corresponds to less autocorrelation in the AR1 corrected model. One lag is ~33 ms, such that the x-axes range from ~0–990 ms.

Fig. 3.

Dependence between running speed, theta/gamma amplitude, and theta phase-gamma amplitude correlations. A: example local field potential (LFP) recordings, with bandpass-filtered versions for theta (th), slow gamma (sg), and fast gamma (fg) frequencies during periods of increasing running speed. In both the raw and filtered traces of the top, slow gamma amplitude increases with running speed. The bottom shows a similar example in which fast gamma amplitude increases with running speed. Vertical dashed lines denote theta troughs. B–D: each panel corresponds to a single term in the base model (see text). Yellow, blue, and red lines on frequency axes denote approximate theta, slow gamma, and fast gamma bands, respectively. B: additive contribution of running speed to power at each frequency. C: additive contribution of theta phase to power at each frequency. Black curve above is the mean broadband (i.e., 0.1–500 Hz) LFP at each phase of theta with scale bar for reference. Notice the asymmetry of the theta waveform. D: additive contribution of the running speed-by-theta phase interaction to power at each frequency. Each subpanel is the additive effect for a separate frequency, denoted by the labels above the subpanels. Frequencies <9.9 Hz are omitted to conserve space as no significant effects were observed at these frequencies. Only every other subpanel axis is labeled for visual purposes. Orange and purple lines denote 0 level contours of the lower and upper 95% simultaneous confidence bands (corresponding pointwise level α < 10⁻⁶), respectively.

Fig. 4.

Experience-dependent trends in theta and gamma power. A: additive contribution of time-by-session to power at each frequency fit under the full model (see text). B: differences between the surfaces shown in A. The left subpanel shows the session 2 surface subtracted from the session 1 surface, while the middle and right subpanels show the session 3 surface subtracted from the session 1 and session 2 surfaces, respectively. For both A and B, the axes on the middle subpanels are not labeled for visual purposes. Orange and purple lines denote 0 level contours of the lower and upper 95% simultaneous confidence bands (corresponding pointwise level α < 10⁻⁶), respectively. Yellow, blue, and red lines on frequency axes denote approximate theta, slow gamma, and fast gamma bands, respectively. C: example LFP recordings (top row) from a representative electrode, and bandpass-filtered versions for theta (th), slow gamma (sg), and fast gamma (fg) frequencies, during one second intervals within the first minute of sessions 1–3 for which animals ran at similar speeds (bottom row). The vertical dashed lines denote theta troughs. Note how theta amplitude is largest in session 1, whereas slow gamma amplitude is lowest in session 1.

Fig. 5.

Experience-dependent trends in the speed modulation of theta/gamma amplitude. Additive contribution of the running speed-by-time interaction to power for each frequency and session fit under the full model. Left, middle, and right panels correspond to sessions 1–3, respectively. Each subpanel corresponds to a separate frequency, denoted by the labels above the subpanels, logarithmically spaced between 2 and 100 Hz. Only every other subpanel axis is labeled for visual purposes. Unvisited pixels are colored white. Orange and purple lines denote 0 level contours of the lower and upper 95% simultaneous confidence bands (corresponding pointwise level α < 10⁻⁶), respectively. Yellow, blue, and red lines above individual subpanels denote frequencies within the theta, slow gamma, and fast gamma bands, respectively.

Fig. 6.

Across-session differences in experience-dependent trends in the speed dependence of theta/gamma amplitude. Between-session differences for the surfaces shown in Fig. 5. Left: session 2 surfaces subtracted from the session 1 surfaces. Middle and right: session 3 surfaces subtracted from the session 1 and session 2 surfaces, respectively. As in Fig. 5, each subpanel corresponds to a separate frequency, denoted by the labels on top. Pixels not visited in both sessions are colored white. Orange and purple lines denote 0 level contours of the lower and upper 95% simultaneous confidence bands (corresponding pointwise level α < 10⁻⁶), respectively. Yellow, blue, and red lines above subpanels indicate frequencies within the theta, slow gamma, and fast gamma ranges, respectively.

Fig. 7.

Experience-dependent trends in theta phase-gamma amplitude correlations. A: additive contribution of the theta phase-by-time interaction to power for each frequency and session fit under the full model. Left, middle, and right panels correspond to sessions 1–3, respectively. Each subpanel shows the additive effect for a separate frequency, denoted by the labels above the subpanels. B: difference surfaces for the fits shown in A. Left: session 2 surfaces subtracted from the session 1 surfaces. Middle and right: session 3 surfaces subtracted from the session 1 and session 2 surfaces, respectively. Only every other subpanel axis is labeled for visual purposes. Orange and purple lines denote 0 level contours of the lower and upper 95% simultaneous confidence bands (corresponding pointwise level α < 10⁻⁶), respectively. Blue and red lines above subpanels indicate frequencies within the slow gamma and fast gamma bands, respectively. Frequencies <14.7 Hz are omitted, as no significant effects were observed at these frequencies.

Fig. 8.

Model comparisons. A: overall leave-one-mouse-out cross-validated R² for the full model at each frequency. B: improvement in R² due to including an additional variable. + time indicates improvement beyond the base model after adding time. + time-by-session indicates improvement beyond the + time model after including time-by-session. + speed/phase-by-time-by-session indicates improvement beyond the + time-by-session model after including speed/phase-by-time-by-session, respectively. Similarly, full indicates improvement beyond the + time-by-session model after including both speed-by-time-by-session and phase-by-time-by-session simultaneously. Yellow, blue, and red lines on x-axes denote approximate theta, slow gamma, and fast gamma frequency bands, respectively.