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. 2023 Jan 9;33(2):85–95. doi: 10.1002/hipo.23490

Stimulation‐induced entrainment of hippocampal network activity: Identifying optimal input frequencies

Teryn D Johnson 1, Katherine R Keefe 2, Lara M Rangel 1,✉
PMCID: PMC10068596  NIHMSID: NIHMS1869501  PMID: 36624658

Abstract

The hippocampus contains rich oscillatory activity, with continuous ebbs and flows of rhythmic currents that constrain its ability to integrate inputs. During associative learning, the hippocampus must integrate inputs from a range of sources carrying information about events and the contexts in which they occur. Under these circumstances, temporal coordination of activity between sender and receiver is likely essential for successful communication. Previously, it has been shown that the coordination of rhythmic activity between the lateral entorhinal cortex (LEC) and the CA1 region of the hippocampus is tightly correlated with the onset of learning in an associative learning task. We aimed to examine whether rhythmic inputs from the LEC in specific frequency ranges were sufficient to enhance the temporal coordination of activity in downstream CA1. In urethane‐anesthetized rats, we applied extracellular low‐intensity alternating current stimulation across the length of the LEC. Using this method, we aimed to phase‐bias ongoing neuronal activity in LEC at a range of different frequencies (from 1.25 to 55 Hz). Rhythmic stimulation of LEC at both 35 and 50 Hz increased the proportion of CA1 neurons significantly entrained to the phase of the applied stimulation current. A subset of stimulation frequencies modified CA1 spiking relationships to the phase of local ongoing CA1 oscillations, with each stimulation frequency exerting a unique influence upon downstream CA1, often in frequency ranges outside the target stimulation frequency. These results suggest there are optimal frequencies for LEC–CA1 communication, and that different profiles of LEC rhythms likely have distinct outcomes upon CA1 processing.

Keywords: CA1, lateral entorhinal cortex, local field potential, neural oscillations

1. INTRODUCTION

Neural systems have intricate connectivity, and these connections support vast numbers of possible interactions. Neurons in these systems often receive input from multiple sources, necessitating mechanisms for selectively processing relevant signals from a flood of information. In oscillating systems, repetitive intervals of relative depolarization and hyperpolarization constrain the effectiveness of inputs (Akam & Kullmann, 2012; Bastos et al., 2015; Fries et al., 2008; Nolan et al., 2004; Nowak et al., 1997; Pavlov et al., 2011). This has led to theories such as Communication Through Coherence, which posits that coordination and precise timing of activity between neural systems is necessary for their successful communication (Colgin & Moser, 2010; Fries, 2005; Fries, 2015; Tiesinga & Sejnowski, 2010). In the most straightforward form of this theory, an input region matches the rhythmic activity of a downstream region and aligns its activity such that inputs arrive downstream during periods of relative depolarization. However, the relationships between rhythmic inputs and the specific endogenous oscillations of a region are not always clear. The interconnectivity and heterogeneity of cell populations in downstream regions may support multiple rhythmic circuit interactions and simultaneous oscillatory activity at different frequencies, rendering mixed effectiveness of inputs at specific frequencies. In addition, oscillatory activity is often dynamic, capable of rapid shifts in frequency given changing intrinsic cell properties (e.g., neuromodulation of channel conductances; Herreras, 2016; Hutcheon & Yarom, 2000; Steriade et al., 1993; Teleńczuk et al., 2017). Input regions attempting to optimize their influence upon downstream regions must be able to adapt to changing rhythmic constraints in downstream regions, potentially with an arsenal of temporal activity patterns to match changing windows of opportunity. The task of optimizing spike timing to achieve the greatest impact upon downstream structures is thus extremely complex, with different frequencies of input likely to engage downstream networks uniquely. However, the extent that multiple rhythms facilitate distinct dialogues between regions is not well understood.

We aimed to evaluate the role of rhythms in modifying communication between two highly rhythmic regions, the lateral entorhinal cortex (LEC) and the distal CA1 subregion of the hippocampus. LEC sends both excitatory and long‐range inhibitory projections to CA1 (Barker et al., 2017; Basu et al., 2016; Bloss et al., 2018; Igarashi, Ito, et al., 2014; Kinnavane et al., 2014; Remondes & Schuman, 2002; Witter, 1993), and is thought to convey multimodal information about objects and odors to the hippocampus to facilitate associative learning (Bitzenhofer et al., 2022; Chapuis et al., 2013; Deshmukh & Knierim, 2011; Keene et al., 2016; Knierim et al., 2014; Leitner et al., 2016; Tsao et al., 2013; Wang et al., 2018; Xu & Wilson, 2012). During the presentation of odors in an associative learning task, the development of coherent beta/low gamma (20–40 Hz) oscillations between CA1 and LEC coincides with the development of odor‐specific representations in both regions and correct performance (Igarashi, Lu, et al., 2014). This suggests the existence of coordinated oscillatory activity during the presentation of stimuli from learned associations, and a potential optimal frequency range for their communication. The CA1 region of the hippocampus manifests a number of additional oscillations that are thought to similarly facilitate selective communication with specific input regions (Bibbig et al., 2007; Colgin et al., 2009; Rangel et al., 2016; Schomburg et al., 2014; Sirota et al., 2003). Given the rich and dynamic oscillatory profile of CA1, a range of possible rhythmic interactions may exist between LEC and CA1.

To better understand how rhythms shape communication between LEC and CA1, we electrically stimulated LEC at a range of frequencies (from 1.25 to 55 Hz) in urethane anesthetized rats. We then tested for changing relationships between CA1 spike timing and (A) the phase of induced LEC oscillations and (B) the phase of local ongoing CA1 oscillations. In this manner, we aimed to characterize the impact of specific stimulation frequencies upon both LEC–CA1 communication and CA1 neuron engagement in other ongoing rhythmic circuit interactions.

2. METHODS

2.1. Rats

All animal procedures were performed in accordance with National Institutes of Health (NIH) and Boston University Institutional Animal Care and Use Committee (IACUC) guidelines. Subjects were six male Long‐Evans rats (Charles River Laboratories), between 6 and 12 months old. Rats were housed individually and maintained on a 12‐h light/dark cycle. All neural recordings were performed during the light cycle. Rats received food and water ad libitum. Weights ranged from 450 to 500 g.

2.2. Neural recordings

Signals were amplified by a preamplifier 20× and amplified again to 4000–6000× (Plexon, Dallas, TX), with a band‐pass filter of 400–8000 Hz to digitally isolate spikes (OmniPlex, Plexon). Local field potentials (LFPs) were digitally isolated with a band‐pass filter from 1 to 400 Hz. LFP and spike channels were globally referenced to a skull screw above the cerebellum, and spike channels were also locally referenced to a wire with no spiking activity. Each rat was surgically implanted with a microdrive containing a bundle of four tetrodes. Each tetrode was composed of four strands of 0.0005″ (12 μm) Nickel‐Chrome wire (Sandvik, Stockholm, Sweden) that were gold‐plated to reduce impedance to 200–250 kOhms at 1000 Hz. The implant site was located over the right dorsal hippocampus (A/P = −4.0 mm; M/L = 2.2 mm), and tetrodes were turned down ~1.9 mm into the brain immediately following surgery to target the CA1 pyramidal layer. In two rats, an additional electrode was implanted between two stimulation electrodes (AP: −5.93, ML: −4.25, DV: −7.4), to record the LEC LFP. A ground electrode was placed over ipsilateral frontal cortex. Final tetrode locations were visualized via a Nissl stain in 40 μm coronal sections. Single units were isolated in Offline Sorter (Plexon) by comparing waveform features across tetrode wires including peak and valley voltage amplitudes, total peak‐to‐valley distance, and principal component analysis. The number of neurons isolated per subject ranged between 16 and 29 unique cells per rat. Sample traces of the CA1 LFP are shown in Figure 1C and Figure S1B during non‐stimulation and 35 Hz stimulation intervals, respectively. Example single unit traces are shown in Figure 2. All data is available from the corresponding author upon reasonable request.

FIGURE 1.

FIGURE 1

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(a) Schematic of the LEC stimuation protcol. The 20‐s stimulation epochs were followed by 30‐s non‐stimulation epochs. Different stimulation frequencies were applied in a random order. (b) Representative histology indicating wire placement in the lateral entorhinal cortex. (c) Top: Sample 5‐s period of local field potential activity in the CA1 during a non‐stimulation epoch. Bottom: Sample 5‐s spectrogram (1/f normalized) of the local field potential activity shown in the above raw trace. (d) Top: Stimulation cycle‐averaged spectrograms (1/f normalized) of CA1 local field potential activity during each stimulation epoch, averaged across all subjects. Bottom: Power spectral density estimates of CA1 local field potential activity for each stimulation epoch, averaged across all subjects.

FIGURE 2.

FIGURE 2

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Left: Example waveforms for two cells, with thick lines indicating the mean waveforms and thin lines indicating standard deviations across the four wires of a tetrode. Right: Circular histograms depicting the number of spikes occurring at different phases of the stimulation frequency. Each row depicts the activity across the stimulation epochs. The p‐values above each plot were calculated using a Rayleigh statistic.

2.3. Urethane anesthesia

Each rat was anesthetized with urethane (1.2 g/kg, i.p.). Urethane induces a long‐lasting, steady level of surgical anesthesia with muscle relaxation, but maintains network oscillations in the brain that are critically dependent on GABAergic neurotransmission (Hara & Harris, 2002; Hunter & Milsom, 1998; Maggi & Meli, 1986a, 1986b). Time of onset for urethane effects ranged from 1 to 2 h. Paw withdrawal reflex was monitored to assess level of anesthesia. Urethane anesthesia was supplemented with ketamine injections (0.09 ml, i.m.), spaced ~1–2 h apart throughout the surgery. Ketamine injections were not administered at least 30 min prior to neural recordings. Additional urethane was not administered in order to prevent the rat from entering an irreversible slow wave state (Hara & Harris, 2002). A wire implanted in the contralateral cortex (A/P = −4.0 mm; M/L = −2.2 mm) allowed us to monitor cortical activity and ensure that the rat had not entered a slow wave state.

2.4. Alternating current stimulation

To stimulate neurons spanning the length of the lateral entorhinal cortex (LEC), two electrodes were implanted at a 20° angle at the anterior and posterior ends of the rat LEC (AP: −4.8 mm, ML: −4.5 mm, DV: −7.4 mm and AP: −7.06 mm, ML: −4.25 mm, DV: −6.8 mm). A sinusoidal alternating current (10 mV, ~0.7–0.9 mA) was then applied at 1.25, 8, 20, 25, 30, 35, 40, 45, 50, or 55 Hz. Each stimulation block was presented once, lasted 20 s, and was both proceeded and followed by 30‐s baseline (non‐stimulation) blocks (Figure 1a). Sinusoidal alternating current stimulation has been used effectively to rhythmically bias the timing of cell spiking activity to match the frequency of the applied alternating current (Ali et al., 2013; Ozen et al., 2010; Reato et al., 2013), and is a preferred method for biasing spike timing without altering overall firing rates (see also Section 3.2 below). For three of the six rats, the order of stimulation frequency was randomized. The stimulation protocol for the other three rats proceeded in order of increasing frequency. A final baseline block ranging from 90 to 328 seconds was recorded at the end of each session.

2.5. Spectral analyses

To characterize the local neural oscillations that manifested in CA1 during different phases of the experiment, the power spectral density (PSD) was calculated after first applying a 59–61 Hz notch filter to remove 60 Hz electrical noise. The average PSD across subjects was first calculated for a 20‐s baseline interval at the end of the experiment (Figure S1A), and averaged across subjects. A PSD was then calculated for each stimulation block type such that a separate average PSD was created for each stimulation frequency (Figure 1d).

Spectrograms during baseline and stimulation intervals were also calculated using a Gabor transform (Leventhal et al., 2012). A 59–61 Hz notch filter was applied to remove 60 Hz electrical noise (Figures 1c, d and S1C). Cycle‐triggered spectrograms of the CA1 LFP were created by aligning the computed spectrograms for each stimulation cycle and averaging across cycles. The cycle‐triggered spectrograms were then normalized by 1/f. Frequency bands of interest for further spike–phase analysis were visually identified as the most prominent rhythms in baseline and stimulation interval PSDs and spectrograms.

2.6. Firing rate analysis

To determine the effect that stimulation might have upon individual neuron firing rates, the average firing rate was calculated for each baseline and stimulation block for all recorded neurons. A repeated measures ANOVA was used to determine if there were any significant increases or decreases in firing rate across baseline and stimulation blocks.

2.7. Assessing spiking relationships to stimulation or LFP phase

To assess CA1 spiking relationships to LEC stimulation phase, the stimulation phase at the time of each spike was first calculated for each CA1 neuron. All identified neurons were included in the study, regardless of firing rate. Significant entrainment to LEC stimulation phase was then assessed using a Rayleigh statistic, and the proportion of neurons in each rat with significant (p < .05) spiking relationships to the LEC stimulation phase was calculated. The average proportion of significantly entrained cells was calculated across rats for each stimulation block. To determine whether the proportion of neurons with significant entrainment was higher during stimulation blocks than during baseline intervals, a bootstrapped baseline distribution of average proportions was created by randomly sampling a 20‐s interval from the baseline block at the end of the experiment 1000 times and calculating spiking relationships to a pure sinusoid (with frequency identical to each stimulation block type). A Z‐score of the average proportion of significant cells for each stimulation block was then compared against the mean and standard deviation of the bootstrapped baseline distribution. Z‐scores were used for comparison purposes due to different levels of neuronal engagement between the different frequency bands of interest. Of note, LEC projects to the distal dendrites of CA1 pyramidal neurons, and it has previously been reported that low‐pass filtering of high‐frequency inputs occurs along CA1 pyramidal neuron dendrites (Vaidya & Johnston, 2013). It is thus possible that spiking relationships to the phase of LEC stimulation may be minimal at high gamma frequencies >80 Hz (Buzsáki & Schomburg, 2015; Vaidya & Johnston, 2013). However, previous studies have observed peaks in CA1 spiking relationships to oscillations in the medial entorhinal cortex LFP at 60 Hz relative to other neighboring frequencies (Schomburg et al., 2014), and thus we anticipate that we will be able to capture spiking relationships to changing input frequencies in the 1.25–55 Hz range.

To assess changing spiking relationships to rhythms in the local CA1 LFP, the CA1 local field potential was first bandpass filtered using a 3rd‐order Butterworth filter according to prominent rhythms identified in baseline and stimulation interval PSDs and spectrograms (see Section 2.5 above and Section 3.1 below). Specifically, the CA1 exhibited large amplitude oscillations in the 15–55 Hz range during both baseline and stimulation epochs, which we further divided into three frequency bands for our analyses: 15–25 Hz, 30–40 Hz, and 45–55 Hz. Phase estimates for each time point were calculated as the arctangent of the complex Hilbert transform. For each neuron, significant entrainment to the phase of a local CA1 rhythm was assessed using a Rayleigh statistic. The average proportion of neurons with significant (p < .05) entrainment to a given CA1 rhythm was then calculated across rats for each stimulation frequency. To determine whether the average proportion of cells entrained to a given CA1 rhythm was greater during a stimulation block than during baseline, a bootstrapped distribution of average proportions was created by randomly sampling 20‐s intervals of the baseline block at the end of the experiment for each rat. A Z‐score of the average proportion of significant cells for each stimulation block was then compared against the mean and standard deviation of the bootstrapped baseline distribution. A Bonferroni corrected significance threshold (p < .005) was used for the comparison of multiple stimulation blocks to the bootstrapped distribution.

3. RESULTS

3.1. Spectral features of CA1 during urethane anesthesia

Power spectral density estimates (Figure 1d and spectrograms (Figure 1d) revealed that there is consistently high beta and low gamma (15–55 Hz) power in the CA1 local field potential (LFP) across all rats and throughout each recording session. This broad frequency range consistently included rising power between 15 and 25 Hz, a peak in power between 30 and 40 Hz, and falling power between 45 and 55 Hz (Figure S1A). Given that 15–55 Hz encompasses a broad range of frequencies, we assessed CA1 spiking relationships to the rise, peak, and fall ranges separately. These separate analyses allowed us to assess whether rhythmic LEC inputs differentially influenced CA1 spiking relationships to these narrower LFP frequency ranges.

3.2. Local LFP amplitude and firing rate differences across stimulation frequencies

There were no significant differences in 15–25 Hz (repeated measures ANOVA, d.f. = 9, p = .2356), 30–40 Hz (repeated measures ANOVA, d.f. = 9, p = .4429), or 45–55 Hz (repeated measures ANOVA, d.f. = 9, p = .3734) CA1 LFP amplitude across stimulation blocks. There were no significant differences in cell firing rates across stimulation blocks (repeated measures ANOVA, d.f. = 9, p = .5457).

3.3. Optimal LEC stimulation frequencies for entraining downstream CA1 neurons

Varying the LEC stimulation frequency modified CA1 spiking relationships to 1) the phase of the LEC stimulation frequency and 2) the phase of CA1 LFP oscillations. We assessed whether the proportions of neurons with significant spike–phase relationships to either an LEC stimulation frequency (Figures 2 and 3) or a local CA1 band of interest (Figures 4 and 5) differed substantially during stimulation intervals from the proportions observed during non‐stimulus baseline intervals using a bootstrapping method described in Section 2.7. Circular histograms were constructed for representative cells, depicting the number of spikes that occurred at different phases of the LEC stimulation frequency (Figure 2) or different phases of the 15–25 Hz, 30–40 Hz, or 45–55 Hz filtered CA1 LFP (Figure 4).

FIGURE 3.

FIGURE 3

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Z‐score of the average proportion of cells significantly entrained to the stimulation phase across all subjects, compared against proportions observed during 1000 randomly selected non‐stimulation baseline epochs. Blue dashed lines represent significance thresholds of p < .05, corresponding to proportions below 5% and above 95% of proportions observed during the baseline.

FIGURE 4.

FIGURE 4

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Circular histograms depicting the number of spikes occurring at different phases of the 15‐25 Hz, 30‐40 Hz, or 45‐55 Hz filtered CA1 local field potential signal. Each row represents the activity of a single neuron across stimulation epochs. The p‐values above each plot were assessed using a Rayleigh statistic.

FIGURE 5.

FIGURE 5

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Left: Histograms of the average proportions of cells with significant entrainment to the phase of a filtered CA1 local field potential (LFP) signal (a: 15–25 Hz filtered CA1 LFP; b: 30–40 Hz filtered CA1 LFP; c: 45–55 Hz filtered CA1 LFP) during 1000 randomly selected baseline epochs across all subjects. Thick black vertical dashed lines depict a Bonferroni corrected significance threshold for the bootstrapped distribution, corresponding to 99.5%. Gray vertical lines indicate proportions of entrained cells observed during a stimulation interval that did not differ significantly from the baseline distribution. Red vertical lines indicate proportions of entrained cells observed during a stimulation interval that deviated significantly from the baseline distribution (with corresponding stimulation frequency noted). Right: Z‐score of the average proportion of cells entrained to the phase of a filtered CA1 local field potential signal across all subjects, compared against proportions observed during 1000 randomly selected non‐stimulation baseline epochs. Blue dashed lines represent significance thresholds of p < .005, corresponding to proportions above 99.5% of proportions observed during the baseline.

Higher proportions of CA1 neurons exhibited significant (Rayleigh statistic, p < .05) entrainment to the phase of the LEC stimulation frequency during 35 Hz (z‐test, p = .0033) and 50 Hz (z‐test, p = .00002) stimulation epochs, and a lower proportion of neurons exhibited significant entrainment to the phase of the LEC stimulation frequency during 30 Hz stimulation (z‐test, p = .0139) (Figures 3, S3, and S4A). Stimulation of LEC at 50 Hz significantly (z‐test, p = .00031) increased the proportion of neurons entrained to the 15–25 Hz filtered CA1 LFP (Figures 5 and S4B–D). In contrast, a higher proportion of CA1 neurons exhibited significant entrainment to the 30–40 Hz filtered CA1 LFP during 20 Hz (z‐test, p = .0000002) and 30–45 Hz (z‐test; 30 Hz: p = .00002; 35 Hz: p = .00002; 40 Hz: p = .0033; 45 Hz: p = .001) LEC stimulation. Finally, 35 Hz (z‐test, p = .0019) and 55 Hz (z‐test, p = .00043) LEC stimulation increased the proportion of significantly entrained CA1 neurons to the 45–55 Hz filtered CA1 LFP. Figure 6 summarizes the changes in entrainment due to varying stimulation frequency.

FIGURE 6.

FIGURE 6

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Summary schematic indicating changes in CA1 spike timing given different LEC stimulation frequencies. Gray arrows indicate that a stimulation frequency significantly changed the mean proportion of cells with significant spike–phase relationships to a specific frequency band in the CA1 local field potential only. Blue arrows indicate an additional increase in the mean proportion of CA1 cells with significant spike timing relationships to the phase of the LEC stimulation frequency. Red arrows indicate an additional decrease in the mean proportion of CA1 cells with significant spike timing relationships to the phase of the LEC stimulation frequency.

3.4. No significant relationships between spike–phase relationships, increasing stimulation frequency, CA1 firing rates, CA1 LFP amplitude, or stimulation amplitude

To assess whether spike–phase relationships across different LEC stimulation frequencies covaried with increasing stimulation frequency, the average firing rate of CA1 cells, or the amplitude of specific frequencies in the CA1 LFP, we implemented a linear regression approach. We found no significant relationships between the proportion of entrained CA1 neurons and the stimulation frequency (F‐statistic = 0.131, d.f. = 9, p = .727), the average firing rate of CA1 cells (F‐statistic = 0.184, d.f. = 9, p = .68; Figure S2D), or the amplitude of frequencies of interest in the CA1 LFP (15–25 Hz d.f. = 9, F‐statistic = 0.135, p = .723; Figure S2A; 30–40 Hz d.f. = 9, F‐statistic = 0.0961, p = .763; Figure S2B; 45–55 Hz d.f. = 9, F‐statistic = 3.09, p = .117; Figure S2C). In addition, because increasing the frequency of applied alternating current can result in slight increases in current amplitude, we assessed a potential relationship between the proportion of entrained CA1 cells and the stimulation amplitude as measured from a wire within LEC. There was no significant relationship between these two measures (F‐statistic = 1.88, d.f. = 9, p = .207).

4. DISCUSSION

In this study, we demonstrate that the influence of LEC on CA1 spiking activity is dependent upon LEC input frequency. Rhythmic stimulation of LEC resulted in an increased proportion of CA1 neurons entrained to the phase of stimulation for only a subset of frequencies between 1.25 and 55 Hz, specifically 35 and 50 Hz. Of note, stimulation of LEC at 30 Hz resulted in a decreased proportion of entrained CA1 neurons to the phase of stimulation. These results suggest that there are both optimal and suboptimal input frequencies for LEC–CA1 communication. In addition, rhythmic stimulation of LEC resulted in an increased proportion of neurons entrained to the phase of other ongoing oscillations in the local field potential, and not necessarily those that matched the stimulation frequency. This suggests that rhythmic LEC inputs influence CA1 engagement in other rhythmic circuits, and that rhythmic inputs have a far‐reaching ability to shape the rhythmic circuit interactions of a system.

Rhythmic stimulation of the LEC influenced CA1 spike timing without changing CA1 firing rates or the amplitude of local ongoing oscillations in CA1. This influence did not occur at all frequencies of stimulation, as many stimulation frequencies (1.25–25 Hz, 40 Hz, 45 Hz, and 55 Hz) did not significantly alter CA1 spiking relationships to LEC stimulation phase (Figure 5). Thus, the influence of LEC stimulation did not linearly increase with stimulation frequency. Instead, there were peaks in its influence at 35 and 50 Hz, with a decrease in CA1 spike timing relationships to the phase of LEC stimulation at 30 Hz. The boundaries between decreased and increased levels of engagement were close, presumably separated by 5 Hz or less, indicating narrow frequency ranges for optimal and suboptimal communication. This is worth noting, as previous studies have indicated increased 20–40 Hz entrainment between LEC and CA1 during associative learning tasks (Igarashi, Lu, et al., 2014), which may constitute more than one frequency band in LEC with distinct influences upon downstream CA1 activity according to our results. While it may be unexpected that relatively small differences in stimulation frequency could have very different influences upon downstream networks, it is not unreasonable. A study in the primary visual cortex showed that differences in attentive state can change local oscillations in V1 by <5 Hz, accompanied by drastic differences in coherence between V1 and downstream V4 (Bosman et al., 2012). These results suggest that slight differences in the frequency of V1 rhythms can alter rhythmic coordination, and potentially communication with V4. It is possible that upstream networks such as V1 and LEC leverage these slight changes in frequency to best communicate with networks with narrow rhythmic constraints due to resonance or phasic inhibition.

Local field potential activity in CA1 during our experiments consisted of prominent segments of 15–25 Hz, 30–40 Hz, and 45–55 Hz oscillations. The prominence of these rhythms may be in part be due to the anesthetized state of the rats. While urethane anesthesia has been shown to have minor effects on endogenous rhythms of the hippocampus, ketamine has been shown to produce significantly increased gamma rhythms within the hippocampus in the range of 30–60 Hz lasting up to 3 h after administration (Caixeta et al., 2013; Hakami et al., 2009; Kittelberger et al., 2012; Lasztóczi & Klausberger, 2014; Penttonen et al., 1998; Sharma et al., 2010; Yagishita et al., 2020). As such, it is likely that the prominent oscillations we observed in the beta and gamma frequency range reflects the anesthetized state of our subjects. While the specific results found in this study might be unique to this particular state (i.e., the stimulation frequencies in this study may only produce these results during urethane‐ or ketamine‐induced anesthesia), they highlight  a capacity of the network to flexibly modify cell engagement given different rhythmic inputs. We found that rhythmic stimulation of LEC influenced CA1 engagement in all three of the CA1 LFP frequency ranges of interest, with mostly different stimulation frequencies enhancing engagement in each. Specifically, 50 Hz enhanced engagement in local 15–25 Hz oscillations, 35 and 55 Hz enhanced engagement in 45–55 Hz oscillations, and 20, 30, 35, 40, and 45 Hz enhanced engagement in 30–40 Hz oscillations (Figure 4). In cases where more than one stimulation frequency increased the proportion of neurons entrained to a particular local band, notable dips in the proportion of entrained neurons between optimal stimulation frequencies suggest at least two distinct bands. Interestingly, stimulation frequencies that enhanced the proportion of CA1 neurons entrained to local rhythms often promoted entrainment to frequency bands outside of the stimulation frequency. This may indicate that LEC modifies or jumpstarts additional rhythmic circuit interactions in CA1.

Taken together, we learn that several LEC input frequencies in the 20–55 Hz range exert an influence upon the timing of CA1 spiking activity, aligning CA1 activity with respect to either the phase of LEC inputs or other rhythmic oscillations in the CA1 LFP. Within this range, 20–40 Hz oscillations in LEC have been reported during the presentation of odors in an associative learning task (Igarashi, Lu, et al., 2014). These oscillations become coherent with local oscillations in CA1 as rats learn associations between odors and outcomes. Our findings suggest that this range in fact encapsulates multiple frequency bands with distinct influences upon downstream CA1 activity. This raises questions as to whether different frequencies of LEC input serve distinct purposes in CA1. In the CA1, at least four distinct oscillations in the theta (4–12 Hz), beta (15–35 Hz), low gamma (35–55 Hz), and high gamma (65–90 Hz) frequency ranges have been reported during the presentation of odors in a similar task (Rangel et al., 2016). Notably, successful performance of the task resulted in marked changes in CA1 spike timing relationships to these rhythms, with interneurons often engaging in each of the four rhythms and principal neurons often engaging in only one rhythm during the presentation of odors. It is possible that dynamic LEC activity in the 20–40 Hz range serves as a switch between multiple distinct rhythmic circuit interactions in CA1.

How do rhythmic inputs from the LEC exert their influence? Our initial intuition was that rhythmic inputs would have the ability to pace downstream activity to some degree. We hypothesized that this might occur for a subset of our LEC stimulation frequencies, given theories regarding successful communication through coherence and previous in vivo recording evidence suggesting 20–40 Hz LEC–CA1 coherence during associative learning tasks. This indeed turned out to be the case (at 35 and 50 Hz), but both of these frequencies also enhanced spiking relationships between CA1 neurons and oscillations in the CA1 LFP. In fact, a larger number of LEC input frequencies influenced CA1 engagement in local rhythms (Figure 6). This latter influence may occur through a number of possible mechanisms. For example, it is possible that direct rhythmic input from LEC both paces downstream CA1 and jumpstarts additional processing in the region at different frequency ranges. Additionally, it is possible that LEC exerts an indirect effect upon CA1 through its inputs to dentate gyrus and CA3 (which projects to CA1). As previous studies have reported 30–50 Hz coordination between LEC and dentate gyrus during object learning tasks (Fernández‐Ruiz et al., 2021), this indirect influence may be at play during LEC stimulation at 30 Hz, when 30 Hz LEC stimulation decreases in the proportion of CA1 neurons entrained to the stimulation rhythm (suggesting reduced efficacy of direct 30 Hz LEC input) but still increases entrainment to local 30–40 Hz CA1 rhythms (suggesting enhanced engagement in 30–40 Hz rhythmic circuits through an indirect route). Overall, our results suggest that one input frequency can exert an influence upon the coordination of multiple oscillations.

In conclusion, our results demonstrate that several narrow frequency bands of LEC input between 20 and 55 Hz are optimal for inducing distinct profiles of CA1 neuron engagement in rhythms during the anesthetized state we used in this study. While these exact results might be unique to this single state, they hint at the effects that relatively small changes in input frequency may have upon hippocampal neuron engagement in local and cross‐regional rhythmic circuit interactions. Given the known dynamism of oscillatory activity in both entorhinal and hippocampal regions, and strong links between the occurrence of specific frequency oscillations in these regions and behavior, future assessments of changing hippocampal spiking relationships to local and cross‐regional oscillations during behavior (and to more narrow frequency bands than traditionally assessed) can provide more mechanistic insight into the selective engagement of hippocampal neurons during distinct behaviors or processing states. Overall, our results indicate complex influences of rhythmic inputs, adding to a growing amount of evidence that the recruitment of hippocampal neuron activity must be orchestrated across multiple rhythmic circuits.

CONFLICT OF INTEREST

The authors declare no competing financial interests.

Supporting information

Figure S1. (A) Power spectral density (1/f normalized) for an example 20‐s baseline interval. Red lines designate a segment with rising power between 15 and 25 Hz, blue lines indicate a segment with a peak in power between 30 and 40 Hz, and green lines indicate a segment with falling power between 45 and 55 Hz. (B) Raw LFP for an example 5‐s 35 Hz stimulation interval. (C) Spectrogram (1/f normalized) for the same interval shown in B.

Click here for additional data file. (3.9MB, pdf)

Figure S2. Median values across baseline and stimulation blocks for (A) the amplitude of the 15–25 Hz filtered signal, (B) the amplitude of the 30–40 Hz filtered signal, (C) the amplitude of the 45–55 Hz filtered signal, and (D) cell firing rates. Red lines mark median values, and the edges of each box represent the 25th and 75th percentiles. Vertical dashed lines encompass the entire range of values for each measurement, with large outliers (outside of 99.3%) marked with a red +.

Click here for additional data file. (1.4MB, pdf)

Figure S3. Histograms indicating the proportions of cells with significant spike–phase relationships to a reconstructed sinusoid matching the LEC stimulation frequency during 1000 randomly selected 20‐s baseline intervals. Unique histograms were generated for each stimulation condition: (A) 1.25 Hz, (B) 20 Hz, (C) 25 Hz, (D) 30 Hz, (E) 35 Hz, (F) 40 Hz, (G) 45 Hz, (H) 50 Hz, and (I) 55 Hz. Red vertical lines indicate the average proportion of cells during stimulation blocks with significant spike–phase relationships to the LEC stimulation frequency. Black vertical lines indicate the significance threshold (p < .05), corresponding to the 95% confidence interval of a distribution.

Click here for additional data file. (3.2MB, pdf)

Figure S4. Median proportions of cells with significant spike–phase relationships to (A) the LEC stimulation frequency, (B) the 15–25 Hz filtered CA1 LFP, (C) the 30–40 Hz filtered CA1 LFP, or (D) 45–55 Hz filtered CA1 LFP. Proportions during stimulation blocks were first calculated for each individual rat, and red lines indicate the median values from a distribution of individual rat values. For baseline intervals, the average proportions for each rat were first calculated from 1000 random 20‐s baseline intervals, and red lines indicate the median values from a distribution of individual rat values. The edges of each box correspond to the 25th and 75th percentiles. Vertical dashed lines encompass the entire range of values for each measurement, with large outliers (outside of 99.3%) marked with a red +. Frequencies labeled in red indicate stimulation blocks with significant differences in the number of entrained cells compared to baseline when data is pooled across rats.

Click here for additional data file. (968KB, pdf)

ACKNOWLEDGMENTS

The authors would like to thank Pamela Rivière, Mia Borzello, Christopher Heyman, Dr. Jon Rueckemann, and Dr. Todd Coleman for their insightful discussions and editorial assistance. This work was partially funded by NIH R01 MH51570, the Frontiers of Innovation Scholars Program, the Kavli Institute for Brain and Mind, and the Hellman Foundation, and NIH R03 MH120406.

Johnson, T. D. , Keefe, K. R. , & Rangel, L. M. (2023). Stimulation‐induced entrainment of hippocampal network activity: Identifying optimal input frequencies. Hippocampus, 33(2), 85–95. 10.1002/hipo.23490

Teryn D. Johnson and Katherine R. Keefe contributed equally to this study.

Funding information Frontiers of Innovation Scholars Program; Hellman Foundation; Kavli Institute for Brain and Mind; National Institute of Mental Health, Grant/Award Numbers: R01 MH51570, R03 MH120406; National Science Foundation, Grant/Award Number: DMS‐1042134

DATA AVAILABILITY STATEMENT

The data that support the findings of this study are available from the corresponding author upon reasonable request.

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Figure S1. (A) Power spectral density (1/f normalized) for an example 20‐s baseline interval. Red lines designate a segment with rising power between 15 and 25 Hz, blue lines indicate a segment with a peak in power between 30 and 40 Hz, and green lines indicate a segment with falling power between 45 and 55 Hz. (B) Raw LFP for an example 5‐s 35 Hz stimulation interval. (C) Spectrogram (1/f normalized) for the same interval shown in B.

Click here for additional data file. (3.9MB, pdf)

Figure S2. Median values across baseline and stimulation blocks for (A) the amplitude of the 15–25 Hz filtered signal, (B) the amplitude of the 30–40 Hz filtered signal, (C) the amplitude of the 45–55 Hz filtered signal, and (D) cell firing rates. Red lines mark median values, and the edges of each box represent the 25th and 75th percentiles. Vertical dashed lines encompass the entire range of values for each measurement, with large outliers (outside of 99.3%) marked with a red +.

Click here for additional data file. (1.4MB, pdf)

Figure S3. Histograms indicating the proportions of cells with significant spike–phase relationships to a reconstructed sinusoid matching the LEC stimulation frequency during 1000 randomly selected 20‐s baseline intervals. Unique histograms were generated for each stimulation condition: (A) 1.25 Hz, (B) 20 Hz, (C) 25 Hz, (D) 30 Hz, (E) 35 Hz, (F) 40 Hz, (G) 45 Hz, (H) 50 Hz, and (I) 55 Hz. Red vertical lines indicate the average proportion of cells during stimulation blocks with significant spike–phase relationships to the LEC stimulation frequency. Black vertical lines indicate the significance threshold (p < .05), corresponding to the 95% confidence interval of a distribution.

Click here for additional data file. (3.2MB, pdf)

Figure S4. Median proportions of cells with significant spike–phase relationships to (A) the LEC stimulation frequency, (B) the 15–25 Hz filtered CA1 LFP, (C) the 30–40 Hz filtered CA1 LFP, or (D) 45–55 Hz filtered CA1 LFP. Proportions during stimulation blocks were first calculated for each individual rat, and red lines indicate the median values from a distribution of individual rat values. For baseline intervals, the average proportions for each rat were first calculated from 1000 random 20‐s baseline intervals, and red lines indicate the median values from a distribution of individual rat values. The edges of each box correspond to the 25th and 75th percentiles. Vertical dashed lines encompass the entire range of values for each measurement, with large outliers (outside of 99.3%) marked with a red +. Frequencies labeled in red indicate stimulation blocks with significant differences in the number of entrained cells compared to baseline when data is pooled across rats.

Click here for additional data file. (968KB, pdf)

Data Availability Statement

The data that support the findings of this study are available from the corresponding author upon reasonable request.

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