Temporal dynamics of cholinergic activity in the septo-hippocampal system

Abstract

Cholinergic projection neurons in the medial septum and diagonal band of Broca are the major source of cholinergic modulation of hippocampal circuit functions that support neural coding of location and running speed. Changes in cholinergic modulation are known to correlate with changes in brain states, cognitive functions, and behavior. However, whether cholinergic modulation can change fast enough to serve as a potential speed signal in hippocampal and parahippocampal cortices and whether the temporal dynamics in such a signal depend on the presence of visual cues remain unknown. In this study, we use a fiber-photometric approach to quantify the temporal dynamics of cholinergic activity in freely moving mice as a function of the animal's movement speed and visual cues. We show that the population activity of cholinergic neurons in the medial septum and diagonal band of Broca changes fast enough to be aligned well with changes in the animal's running speed and is strongly and linearly correlated to the logarithm of the animal's running speed. Intriguingly, the cholinergic modulation remains strongly and linearly correlated to the speed of the animal's neck movements during periods of stationary activity. Furthermore, we show that cholinergic modulation is unaltered during darkness. Lastly, we identify rearing, a stereotypic behavior where the mouse stands on its hindlimbs to scan the environment from an elevated perspective, is associated with higher cholinergic activity than expected from neck movements on the horizontal plane alone. Taken together, these data show that temporal dynamics in the cholinergic modulation of hippocampal circuits are fast enough to provide a potential running speed signal in real-time. Moreover, the data show that cholinergic modulation is primarily a function of the logarithm of the animal's movement speed, both during locomotion and during stationary activity, with no significant interaction with visual inputs. These data advance our understanding of temporal dynamics in cholinergic modulation of hippocampal circuits and their functions in the context of neural coding of location and running speed.

Keywords: acetylcholine; darkness; entorhinal cortex; fiber photometry; hippocampus; medial septum; running speed; temporal dynamics.

FIGURE 1

Linear correlation between the population activity of MSDB cholinergic neurons and the logarithm of an animal’s running speed. (A) One video frame showing the DeepLabCut labels for neck, nose, left ear, right ear, tail base, and tail tip. Gray scale of the video shows the temperature of the mouse recorded via a thermal camera. (B) Histological verification of the fiber track position in the MSDB. The arrowhead marks the fiber track. Magenta colors indicate immune-positive staining for ChAT, green colors indicate presence of jGCaMP7s. (C) Immuno-histological data on jGCaMP7s expression in the MSDB neurons. Note the overlap between jGCaMP7s-postive and ChAT-positive neurons. The asterisk marks the tip of the implanted optical fiber. (D) Data on running speed and activity of cholinergic neurons in the MSDB measured via fiber-photometry for one example session. Note the high fluctuations in the fiber photometry signal during low running speeds. (E) Scatter plot of cholinergic activity vs. running speed; data points were sampled in 1-s intervals from the time series data shown in (D). Red line shows the exponential fit to the data. (F) Speed tuning curve of cholinergic activity. Data show mean ± s.e.m. of speed-binned data with a bin width of 1-cm/s; data from 103 sessions recorded from 5 mice. (G) Same data as in (D) but now plotting the logarithm of running speed. (H) Scatter plot of cholinergic activity vs. the logarithm of the animal’s running speed; data points were sampled in 1-s intervals from the time series data shown in (G). Red line shows the linear fit to the data. (I) Tuning curve shows cholinergic activity as a function of the logarithm of running speed; data show mean ± s.e.m. of binned data with a bin width of 0.1; data from 103 sessions recorded from 5 mice; (J) Speed tuning curves of cholinergic activity comparing data from light (red, n = 55) and darkness (black, n = 47) sessions; data presented as in (F) (K) data presented as in (I) comparing data from light (red, n = 55) and darkness (black, n = 47) sessions; no significant difference between light and darkness sessions was found (see Table 2 for statistics). R, Pearson’s correlation coefficient.

FIGURE 2

The activity of cholinergic neurons is linearly correlated to neck movements during stationary behavior. (A) Data on the speed of neck movements during stationary behaviors and activity of cholinergic neurons in the MSDB measured via fiber-photometry for one example session, in which the mouse remained stationary (running speed < 3-cm/s) for more than two thirds of the session length. Note the high fluctuations in the fiber photometry signal during stationary activity. (B) Scatter plot of cholinergic activity vs. running speed; data points were sampled in 1-s intervals from the time series data shown in (A). Red line shows the exponential fit to the data. (C) Speed tuning curves of cholinergic activity comparing data from light (red, data from 13 sessions, 3 mice) and darkness (black, data from 3 sessions, 2 mice) sessions. Data show mean ± s.e.m. of speed-binned data with a bin width of 1-cm/s. (D) Same data as in (A) but now plotting the logarithm of neck movement speed. (E) Scatter plot of cholinergic activity vs. the logarithm of the animal’s neck movement speed; data points were sampled in 1-s intervals from the time series data shown in (D). Red line shows the linear fit to the data. (F) Tuning curve shows cholinergic activity as a function of the logarithm of neck movement speed; data show mean ± s.e.m. of binned data with a bin width of 0.1; data on light sessions (red) from 13 sessions, 3 mice; data on darkness sessions (black) from 3 sessions, 2 mice; no significant difference between light and darkness sessions was found (see Table 3 for statistics). R, Pearson’s correlation coefficient.

FIGURE 3

Temporal dynamics in cholinergic activity align with changes in movement speed. (A) Data show Pearson’s correlation coefficients between moving averages of cholinergic activity and the animal’s movement speed as a function of the time window used for computing the moving average of cholinergic activity. Blue line and shaded area show the mean ± s.e.m. of data from n = 103 sessions from five mice. The average peaks at 1.3-s. (B) Distribution of the optimal time windows, computed for each session, that maximize the correlation between moving averages of cholinergic activity and movement speed. The histogram shows a peak between 0.8 and 1-s. (C,D) Data presented as in (A,B) but comparing data from light (red, n = 55 sessions, five mice) and darkness sessions (black, n = 47, 4 mice). No significant difference was found between light and darkness sessions shown in (C) when comparing values at 1.3-s, t(100) = -0.192, p = 0.85. (E) Subset of data presented in (A) that only includes n = 16 sessions from three mice, in which the mice spent at least two thirds of the session time engaged in stationary behaviors (running speed < 3-cm/s). The average peaks at 1.7-s. (F) Same subset of data on stationary activity as shown in (E). Histogram shows the distribution of the optimal time windows, computed for each session, that maximize the correlation between moving averages of cholinergic activity and neck movement speed during stationary activity. (G-I) Granger causality magnitudes comparing Granger causality directions from the logarithm of movement speed to cholinergic activity (Speed → ACh) and vice versa (ACh → Speed) across animals. Each bar shows data from one mouse. Data are represented as mean ± s.e.m. across sessions. (G) Data from all sessions. (H) Data on light sessions only. (I) Data on dark sessions only. Note that one mouse was not recorded during darkness (see Tables 4, 5 and Supplementary Table 1 for statistics).

FIGURE 4

VAME reveals four distinct behaviors during open field exploration. (A) Unifold Manifold Approximation (UMAP) embedding of the latent representation encoded from VAME’s recurrent neural network, color-coded based on four human expert-labeled behavioral communities corresponding to exploratory running, exploratory walking, grooming, and rearing. (B) Hierarchical representation of the four communities of behavioral motifs in (A). Percentages below each community highlight the time duration observed for that behavior across a total of 10 h of open field exploration (n = 74 sessions). (C–F) Representative video frames for each behavioral community identified with VAME. Videos showing examples of behavioral activities for each identified community are provided in Supplementary material. (G) Z-scored ΔF/F associated with each behavioral community. Horizontal solid white lines indicate the medians (see Table 6 for statistics).