Open Access
Add to BibSonomy
Abstract
The deep layers of medial entorhinal cortex (MEC) are considered a crucial station for spatial cognition and memory. The deep sublayer Va of MEC (MECVa) serves as the output stage of the entorhinal-hippocampal system and sends extensive projections to brain cortical areas. However, the functional heterogeneity of these efferent neurons in MECVa is poorly understood, due to the difficulty of performing single-neuron activity recording from the narrow band of cell population while the animals are behaving. In the current study, we combined multi-electrode electrophysiological recording and optical stimulation to record cortical-projecting MECVa neurons at single-neuron resolution in freely moving mice. First, injection of a viral Cre-LoxP system was used to express channelrhodopsin-2 specifically in MECVa neurons that project to the medial part of the secondary visual cortex (V2M-projecting MECVa neurons). Then, a lightweight, self-made optrode was implanted into MECVa to identify the V2M-projecting MECVa neurons and to enable single-neuron activity recordings in mice performing the open field test and 8-arm radial maze. Our results demonstrate that optrode approach is an accessible and reliable method for single-neuron recording of V2M-projecting MECVa neurons in freely moving mice, paving the way for future circuit studies designed to characterize the activity of MECVa neurons during specific tasks.
© 2023 Optica Publishing Group under the terms of the Optica Open Access Publishing Agreement
1. Introduction
The entorhinal cortex (EC) has classically been considered an intermediary between the hippocampus and neocortex regions [1–6]. The medial entorhinal cortex (MEC) encodes spatial information [7,8], and studies using traditional electrophysiology techniques have identified multiple neuron types with spatially modulated properties, such as grid cells [9], border cells [10], speed cells [11], and head direction cells [12,13] in MEC over decades. These spatially modulated neurons not only deliver spatial location information, but also participate in spatial learning, memory, and navigation [12,14–16]. The deep sublayer Va of MEC (MECVa) is the major source of MEC output [17], sending a large number of long-range projections to > 30 cortical and subcortical areas [18]. Therefore, MECVa serves as a critical connection between the medial temporal lobe and the neocortex. Recent studies have demonstrated the medial part of the secondary visual cortex (V2M) receives substantial projections from MECVa [17,18]. However, the activity patterns of MECVa neurons projecting to the cortex, such as those projecting to V2M, have yet to be fully investigated at single-neuron level in freely moving animals. And investigating the firing patterns of MECVa neurons is essential for a better understanding of their functions.
Addressing this issue presents a challenge, primarily due to the difficulty of monitoring the activity patterns at the single-neuron level in such a narrow band of cell population [17,18] in freely behaving animal. Fiber photometry provides a simple but effective solution for specific projection recording in freely moving animals during behavioral tasks. However, it lacks spatial resolution [4,19,20]. In contrast, two-photon microscopy enables high-resolution and efficient in vivo recording, but is often restricted to superficial brain regions (e.g., neocortex) in anesthetized or head-fixed animals [21,22]. While microendoscopy equipped with gradient refractive index lenses can image neurons in deep brain regions during behavioral tasks, the size of the image lens can be invasive to brain tissue [23,24].
Electrophysiology is a powerful technique to investigate neuronal activity, and conventional electrophysiology has been widely used in MEC-related research. As previously described, Moser et al. used this approach and discovered a series of spatially modulated cells in MEC [10,12,25,26]. However, electrophysiology cannot record individual neurons in specific circuits, which limit its application. Comprising multi-electrode electrophysiological recording and optical stimulation [27], optrode recording approach can accomplish single-neuron recording within a specific neural circuit in freely moving animals [28,29]. Also, optrode recording employs a microdrive for vertical propagation [30], which allows adjustment of recording depth. Moreover, optrodes are lightweight and do not significantly restrict mouse locomotion [30].
Here, we utilized optrode recording to monitor V2M-projecting MECVa neurons at the single-neuron level in freely moving mice. Injection of a Cre–LoxP viral delivery system resulted in specifically expression of channelrhodopsin-2 (ChR2) in V2M-projecting MECVa neurons, and an optrode was implanted into MEC to identify and record the activity of these neurons. Our approach successfully recorded the single-neuron activity of V2M-projecting MECVa neurons in mice during the open field test (OFT) and 8-arm radial maze (8-ARM). Our results indicate that optrode recording is a powerful method to record the activity of individual neurons within a specific neural circuit in animals during behavioral tasks.
2. Materials and methods
2.1 Mice
Adult male C57BL/6J mice (2–4 months old) were used for all experiments. Mice were housed in groups in standard housing conditions at 21–24 °C and 50%–60% humidity with free access to food and water and on a 12-hour light–dark cycle. Mice with an optrode implant were housed singly. All experimental procedures were performed in accordance with institutional animal welfare guidelines and were approved by the Third Military Medical University Animal Care and Use Committee.
2.2 Optrode fabrication
The optrodes were fabricated as previously described [30,31]. Briefly, the self-made optrode was constructed from three major components: tetrodes, a microdrive, and the optics part (Fig. 1). To create the tetrodes, 25-µm-diameter insulated tungsten wire (California Fine Wire, CFW2002936) was used. Four 7-cm-long tungsten wires were twisted into a tetrode by an electromagnetic stirrer (Apera Instruments, 801 Magnetic Stirrer), and heated with a heat gun (∼450°C for ∼1 min) to form a tight adhesion by melting the insulation (Fig. 1(A)). A silica capillary tube (Polymicro Technologies, TSP100170; ID, 100 µm; OD, 164 µm) was then jacketed around the tetrode for support and protection using super glue. Four tetrodes were arranged into a line with 200 µm spacing between adjacent tetrodes, and glued together with super glue. Each electrode was then connected and secured onto a PCB board with a 16-channel connector (Omnetics, A79016-001) by gold pins (Neuralynx, small EIB pins) [30].
Fig. 1. Optrode fabrication procedure. (A) Image showing tetrode preparation; the tetrode was twisted by an electromagnetic stirrer and heated by a heat gun. (B) Image illustrating the preparation of microdrive. (C) Schematic for the optics portion of the optrode and the recording system. The laser diode (LD) was controlled by an LD current controller. Light from the LD was coupled into the optical fiber and delivered to the recording area. The inset in the red circle highlights the light coupling. Electrophysiological signals were amplified by an amplifier and acquired using a USB interface board. The arrows indicate the transmission direction of signals. (D, E) Images illustrating the preparation of the optics part. The LD and fiber were aligned (D). Light from the LD was collimated into the fiber with the assistance of an optical power meter (E). (F) The tetrodes, microdrive and optics part were assembled into an optrode. (G) An image of a completed optrode. The inset picture shows an enlarged tip of the optrode with a 500 µm height difference between the fiber tip and the tetrode tips. (H) Output power at the optrode tips as a function of input voltage. Data from 7 optrodes (marked by different colors) used across the experiments. Dashed line indicates the maximal power (20 mW) used in the experiments.
The microdrive was constructed based on a previous study [32]. Essentially, it consisted of three parts: a metal rod, three plastic blocks, and a screw-nut pair (Fig. 1(B)). The metal rod served as the support for the three plastic blocks, with the block in the middle being tapped to allow for movement when the screw was turned. A nut was fixed at the end of the screw. To reinforce the structure, plasticine and dental cement were applied to the nut and the bottom plastic block (indicated as “base” in Fig. 1(B)).
The optics part was consisted of a laser diode (LD) and an optical fiber (schematic in Fig. 1(C)). To prepare the optical fiber, a 20-mm-long optical fiber (Thorlabs, FT200EMT; 200 µm diameter, NA 0.39) was cut using a fiber cutter. The LD (Osrma, PL450B) was aligned with the fiber (Fig. 1(D)). The light emitted from the LD was collimated into the fiber (Fig. 1(C)). The light power at the fiber tip was measured using an optical power meter (Thorlabs, PM100D) (Fig. 1(E)). The relative position of the LD and fiber was adjusted for optimal coupling. The LD and fiber were connected using a UV-curing optical adhesive (Norland, NOA 61) [33].
At last, the prepared three parts were assembled to form an optrode (Fig. 1(F)). The optical fiber was attached to the tetrode, with the tetrode tips protruding 500 µm beyond fiber tip to ensure sufficient illumination of the recording area (Fig. 1(G)). A piece of sheet metal was used to connect the movable part of microdrive and tetrodes using super glue. Subsequently, the light transmission efficiency was measured (input voltage from 0.2 V to 1.8 V), and summarized in Fig. 1(H). The output power of all optrode covered the power range we used in the experiments (from 5 mW to 20 mW).
2.3 Stereotaxic surgery
For all surgeries, mice were anesthetized with 3% isoflurane in oxygen for 3–5 min and then placed into a stereotactic apparatus (RWD Instruments). The anesthesia was continued at 1.0%–1.5% isoflurane and a heating pad was placed under the mice to maintain a body temperature of ∼37 °C throughout surgery. After surgery, the mice were kept on the heating pad until awake and then sent to cages for full recovery. To reduce inflammation, mice received a single dose of dexamethasone sodium phosphate (1 mg/ml, 0.1 ml/10 g) and ceftriaxone sodium (50 mg/ml, 0.1 ml/10 g) by intraperitoneal injection daily for 3 days after surgery [4,34].
For viral injections, an 8–10-mm-long incision was made along the skull midline and a small craniotomy (0.5 × 0.5 mm) was made with a dental drill. A glass micropipette with a tip diameter of 10–20 µm was used to deliver virus into the target brain region. To label V2M-projecting MECVa neurons with ChR2, 200–250 nl of AAV2/9–EF1α–DIO–ChR2–mCherry (titer: 3.67 × 1012 viral particles/ml, Taitool Bioscience Co., Ltd., Shanghai) were injected into MEC using the following coordinates: anterior–posterior (AP): -4.6 mm; medial–lateral (ML): + 3.5 mm; and dorsal–ventral (DV): 2.4, 2.2, 2.0, 1.8, 1.6. Concurrently, ∼300 nL of retroAAV2/2 Plus–Syn–Cre (titer: 1.92 × 1013 viral particles/ml, Taitool Bioscience Co., Ltd., Shanghai) were injected into V2M using the following coordinates: AP: -3.4 mm; ML: + 1.3 mm; and DV: 0.2 mm. After injection, the scalp was carefully sutured.
Optrodes were mounted on stable bases comprising four stainless steel screws, dental cement with a cylindrical space, and copper grids. For implantation, the optrode assembly was preliminarily placed in MECVa at a depth of 1.2 mm. Ointment was applied to the cylindrical space to keep the interface moist. Additional dental cement was then used to fix the optrode, and the copper grids were trimmed and welded to an enclosure. To protect the inner structure, a self-adhesive bandage was used to cover the top of the entire headset.
2.4 Electrophysiological recording in freely moving mice
After mice had recovered from surgery, the optrode assembly was lowered forward and electrophysiological recordings were made each day. Electrophysiological signals were amplified using a 16-channel digital amplifier (Fig. 1(C), Intan Technology, C3334) and acquired at 20 KHz by an RHD USB interface board (Fig. 1(C), Intan Technology, C3100). For identification of V2M-projecting MECVa neurons, excitation pulses were released from the LD under the control of an LD current controller (Fig. 1(C), Thorlabs, LDC205c). Each stimulation lasted for 10 ms at a low (2 Hz), moderate (10 Hz), or high (20 Hz) frequency. The ramp test contained 5 stimulation trials: low-frequency stimulation at 5 mW, 10 mW, and 20 mW, and moderate- and high-frequency stimulations at 20 mW.
Electrophysiological data and videos of mouse behavior were synchronously recorded when mice were introduced into the behavioral test arena. Event marks were given for offline synchronization. To verify the sites of the optrodes after electrophysiological experiments were complete, mice were subjected to electrical lesion with a 30 µA current lasting for 10 seconds.
2.5 OFT
OFTs were performed in a wooden box (50 cm × 50 cm × 50 cm) that was cleaned with a 75% alcohol solution and then sprinkled with chocolate crumbs randomly before each test to increase the exploratory motivation [11,35]. Mice were allowed to freely explore the test arena for 20–30 minutes.
2.6 8-ARM
The 8-ARM consisted of eight arms radiating from a central platform [36]. The radiating arms were 50 cm long × 10 cm wide and the wall was 10 cm tall. A hole filled with water was 1.5 cm away from the end of each arm and could not be seen from the central platform. Visual cues were stuck to the wall and served as spatial references. Mice were placed on the central platform and allowed to freely explore in the maze for 10–20 min.
2.7 Histology
Mice were euthanized by anesthetic overdose followed by perfusion with 0.9% saline and then with 4% paraformaldehyde (PFA) in phosphate-buffered saline (PBS; 154 mM NaCl, 18 mM NaH2PO4 ▪ 2H2O, 76 mM Na2HPO4, pH 7.4) 48 hours after the electrical lesion. Perfused brains were dehydrated in 15% sucrose in PBS for at least 24 hours, then sagittally sectioned into 50-µm slices. The slices were mounted on slides, stained with DAPI, and imaged using a wide-field (Olympus, BX51) or confocal (Zeiss, LSM 700) fluorescence microscope.
2.8 Data processing and statistical analysis
The procedure for data preprocessing has been previously published [4,31] and was used to extract spikes in electrophysiological recording data. Then, a predetermined amplitude threshold was set at four standard deviations above the baseline to filter ambiguous events, and events that exceeded the threshold were saved to practice the subsequent spike sorting analysis. Based on the features of waveforms [37], the toolbox MClust was used to sort the detected events for each tetrode, then cells were manually classified according to firing pattern.
Statistical tests were performed with MATLAB. Statistical differences were calculated using the two-tailed Wilcoxon rank-sum test. All summarized data are presented as mean ± SEM.
3. Results
3.1 Specific labeling of V2M-projecting MECVa neurons with ChR2
As recently reported, neurons in MECVa send a widely distributed projection to both cortical and subcortical areas in mouse brain [5,18,38,39]. Among the downstream brain regions of MECVa, V2M receives extensive efferent from MECVa [18]. Thus, we selected V2M-projecting MECVa neurons for labeling to study MECVa projection. For labelling, a retrograde virus expressing Cre recombinase was injected into the V2M and a Cre-dependent AAV expressing DIO–ChR2–mCherry was injected into the deep layers of MEC (Fig. 2(A)). Confocal images confirmed abundant and specific expression of the light-sensitive channel protein ChR2 (red) in MECVa neurons (Fig. 2(B)).
Fig. 2. Labeling of V2M-projecting MECVa neurons with ChR2. (A) Schematic diagram of viral injection. Top: RetroAAV expressing Cre was injected into V2M; bottom: a Cre-dependent AAV expressing DIO–ChR2–mCherry was injected into MECVa. (B) Representative histological images showing expression of ChR2 in MECVa; bottom right panel is the magnified area of white rectangle in the top right panel.
3.2 Optrode implantation into MECVa
The self-made 16-channel optrode was targeted at MECVa, and the activity of single neurons was recorded in Fig. 3(A). And the serial sections were used to determine the location of fibers and tetrode tips in MEC (Fig. 3(B)). The positions of the optrodes that had tagged cells (n = 7) were shown in position reconstruction images (Fig. 3(C)), indicating that optrodes were successfully implanted into MECVa. Mice received dexamethasone and ceftriaxone sodium to alleviate pain and inflammation after optrode implantation. Mouse weight was monitored throughout the experiment, and we found that animals lost an average of less than 10% of normalized body weight (Fig. 3(D)).
Fig. 3. Implantation of self-made optrode into MECVa. (A) Diagram of optrode implantation in MECVa. (B) Virus expression and locations of fiber and tetrode tips in MEC in serial sagittal sections. Arrowheads identify four tetrodes; the yellow arrowhead identifies the tetrode that had tagged a cell. R, rostral; C, caudal; D, dorsal; V, ventral. (C) Summary of tetrode locations, n = 7 mice. (D) Normalize weight change across days after optrode implantation surgery, n = 7 mice. Dashed line indicates 10% body weight loss.
3.3 Identification of V2M-projecting MECVa neurons with the optrode approach
A light stimulation series was conducted to identify V2M-projecting MECVa neurons before (Epoch 1) and after (Epoch 2) behavioral tests were performed. We found that the performance of the optrode was similar in Epoch 1 compared with Epoch 2 (Fig. 4(A)), indicating the stability of the recording. Time histograms of light-evoked spikes recorded during Epoch 1 are shown in Fig. 4(B). Figure 4(C) displays the waveforms of a representative neuron recorded from four channels of a tetrode, and the correlation coefficient between the averaged light-evoked spike (red line) and the spontaneous spike (grey line) is 0.99.
Fig. 4. Identification of V2M-projecting MECVa neurons using optical stimulation. (A and B) Raster plots (A) and histograms (B) of the firing sequences upon optical stimulation in a representative V2M-projecting MECVa neuron at indicated stimulation frequencies and power; Epoch 1 and Epoch 2 represent stimulation before and after behavior experiments, respectively. (C) Waveforms of spontaneous (gray, superimposed) and light-evoked (red, averaged) spikes from the neuron in A. Vertical scale bar, 0.2 mV; horizontal scale bar, 0.2 ms. (D) Distribution of the latencies of all recorded V2M-projecting MECVa neurons, n = 11. (E) Distribution of correlation coefficients between light-evoked and spontaneous spikes for all recorded V2M-projecting MECVa neurons, n = 11. (F) Success rate versus temporal jitter of the first light-induced spikes for all recorded V2M-projecting MECVa neurons, n = 11.
In total, we recorded 11 neurons in MECVa that responded to light stimulation. The response latencies, defined as the time from the onset of light stimulation to the appearance of the spike, ranged from 1.7 to 3.9 ms (Fig. 4(D)). Among the 11 neurons, the minimum correlation coefficient between light-evoked and spontaneous spikes was 0.87 (Fig. 4(E)). Jitter was used to calculate the standard deviation of the response latency, and a lower jitter value represented a more stable response of the neuron. The jitter values of all 11 neurons were < 2.09 ms (Fig. 4(F)). The success rate, defined as the number of light-induced spikes divided by the number of optical stimuli, of all recorded neurons was > 70% (Fig. 4(F)). These results suggest that the optrode recording approach is an accessible and reliable method for single-neuron recording of V2M-projecting MECVa neurons.
3.4 Single-neuron recordings of V2M-projecting MECVa neurons during the OFT
For the OFT, mice were placed in a square arena and allowed to freely explore while single-neuron activities of V2M-projecting MECVa neurons and video were recorded (Fig. 5(A)). The electrophysiological data and behavioral videos were analyzed offline to extract spikes and to map the mouse trajectory (Fig. 5(B)). The neurons that responded to optical stimulation were identified as V2M-projecting MECVa neurons (tagged neurons), whereas neurons recorded by the same tetrode that did not respond to light stimulation were supposed as non-V2M-projecting MECVa neurons (untagged neurons). The firing patterns of two V2M-projecting MECVa neurons are shown in Fig. 5(B), and the firing patterns of two non-V2M-projecting MECVa neurons are shown in Fig. 5(C). Next, we calculated the average firing rates of V2M-projecting MECVa neurons and non-V2M-projecting MECVa neurons during the OFT, which showed that the average firing rate of V2M-projecting MECVa neurons was significantly higher than that of the non-V2M-projecting MECVa neurons (tagged neurons, 8.90 Hz, n = 11; untagged neurons, 2.55 Hz, n = 17; two-sided Wilcoxon rank-sum test, ** P < 0.01; Fig. 5(D)).
Fig. 5. Single-neuron recordings of V2M-projecting MECVa neurons during the OFT. (A) Schematic diagram of recording setup in the open field. (B) Spike locations (red dots) of two representative V2M-projecting MECVa neurons and the corresponding movement trajectory (yellow line) in the open field. Top: average waveforms of light-evoked spikes (red) and spontaneous spikes (black); scale bar, 200 mV. (C) Spike locations (red dots) of two representative non-V2M-projecting MECVa neurons and the corresponding movement trajectory (yellow line) in the open field. Top: average waveform of spontaneous spikes; scale bar, 200 mV. (D) Summary of the firing rates of V2M-projecting (n = 11 neurons from 7 mice) and non-V2M-projecting (n = 17 neurons from 7 mice) MECVa neurons, Wilcoxon rank-sum test, **P < 0.01.
3.5 Single-neuron recordings of V2M-projecting MECVa neurons in the 8-ARM
We also recorded the activities of V2M-projecting MECVa neurons of mice navigating a more complicated maze, the 8-ARM (Fig. 6(A)). The firing patterns of two V2M-projecting MECVa neurons are shown in Fig. 6(B), and the firing patterns of two non-V2M-projecting MECVa neurons are shown in Fig. 6(C). Consistent with our findings in the OFT, the average firing rate of V2M-projecting MECVa neurons was significantly higher compared with that of non-V2M-projecting MECVa neurons (tagged neurons, 10.21 Hz, n = 11; untagged neurons, 2.65 Hz, n = 17; two-sided Wilcoxon rank-sum test, ** P < 0.01; Fig. 6(D)).
Fig. 6. Single-neuron recordings of V2M-projecting MECVa neurons in the 8-ARM. (A) Schematic diagram of recording setup in the 8-ARM. (B) Spike locations (red dots) of two representative V2M-projecting MECVa neurons and the corresponding movement trajectory (yellow line) in the 8-ARM. Top: averaged waveforms of light-evoked spikes (red) and spontaneous spikes (black); scale bar, 200 mV. (C) Spike locations (red dots) of two representative non-V2M-projecting MECVa neurons and the corresponding movement trajectory (yellow line) in the 8-ARM. Top: averaged waveform of spontaneous spikes; scale bar, 200 mV. (D) Summary of the firing rates of V2M-projecting (n = 11 neurons from 7 mice) and non-V2M-projecting (n = 17 neurons from 7 mice) MECVa neurons, Wilcoxon rank-sum test, **P < 0.01.
4. Discussion
Optrodes are a newly developed, powerful tool to record neuronal firing patterns in freely moving mice [27]. Here, we specifically labeled V2M-projecting MECVa neurons using a virus-based labeling tool [40] and identified V2M-projecting MECVa neurons by optical stimulation. Implanted optrodes successfully recorded single-neuron activities of V2M-projecting MECVa neurons during the OFT and 8-ARM, demonstrating that the optrode recording method is suitable for single-neuron recording of specific projecting neurons during behavioral tasks.
The field of neuroscience has been progressed over the years by improving technologies to monitor neuronal activities. Fiber photometry is an effective method to monitor the neuronal population Ca2+ activity [41–43], but this approach lacks the capability of recording at single-neuron resolution. Conventional electrophysiological techniques provide single-neuron recording [28]; however, they are unable to target and record neurons of a specific cell type or in a specific circuit. Recently developed miniaturized two-photon microscopy has enabled approaches to study relationships between neuronal firing and complex behavioral paradigms, but with the drawback that placement of an invasive large-diameter lens causes damage to brain tissue [24,44–46].
Compared to the aforementioned techniques, optrode recording possesses several advantages. First, optrodes combine multi-electrode electrophysiological recording and optical stimulation [29,30], and thus they can identify individual V2M-projecting MECVa neurons. Second, optrodes employ a microdrive for vertical propagation, which allows adjustment of the recording depth. Third, the stable implantation of an optrode ensures that the recording system produces consistently high-quality recordings. Fourth, our self-made, lightweight optrode [28,30] connects to the recording device with a highly flexible cable, which allows free, natural behaviors of mice in a test arena. Finally, minimally invasive surgical implantation of an optrode is associated quick post-procedure recovery and minimal weight loss. Collectively, the attributes of optrodes and our data indicate that this method can be adapted for a broad array of behavioral studies, such as object recognition and social memory tests.
There are several deficiencies in this method that should be considered. Our approach only used virus carrying ChR2, but cell-type-specific viral tools engineered with specific promoters could be used to reveal functions of different neuron subtypes [47]. Transgenic mice could also be employed to explore neuronal functions in specific brain areas and in specific types of neurons [1,5]. Also, our self-made optrode was designed with 16-channel electrodes, electrodes with more channels would be expected to provide higher recording efficiency.
5. Conclusion
In this study, we established an accessible method to investigate V2M-projecting MECVa neuronal activity in freely moving mice using a self-made optrode. Our results suggest that this method may be ideal for future studies investigating neural firing patterns of MECVa at the circuit level during a variety of relevant tasks.
Funding
National Natural Science Foundation of China (32127801, 31925018, 31921003, 32200838, 32171096); National Key Research and Development Program of China (2021YFA0805000); Postdoctoral Science Foundation (2020M673651); Chongqing Basic Research grants (cstc2019jcyjjqX0001).
Acknowledgments
The authors are grateful to Ms. Jia Lou for help in composing and layout editing of the figures. X.C. is a member of the CAS Center for Excellence in Brain Science and Intelligence Technology.
Disclosures
The authors declare no conflicts of interest.
Data availability
Data underlying the results presented in this paper are not publicly available at this time but may be obtained from the authors upon request.
References
1. J. Suh, A. J. Rivest, T. Nakashiba, T. Tominaga, and S. Tonegawa, “Entorhinal cortex layer III input to the hippocampus is crucial for temporal association memory,” Science 334(6061), 1415–1420 (2011). [CrossRef]
2. S. J. Zhang, J. Ye, J. J. Couey, M. Witter, E. I. Moser, and M. B. Moser, “Functional connectivity of the entorhinal-hippocampal space circuit,” Philos. Trans. R. Soc. Lond., B, Biol. Sci. 369(1635), 20120516 (2014). [CrossRef]
3. K. Takehara-Nishiuchi, “Entorhinal cortex and consolidated memory,” Neurosci. Res. 84, 27–33 (2014). [CrossRef]
4. H. Qin, L. Fu, B. Hu, X. Liao, J. Lu, W. He, S. Liang, K. Zhang, R. Li, J. Yao, J. Yan, H. Chen, H. Jia, B. Zott, A. Konnerth, and X. Chen, “A visual-cue-dependent memory circuit for place navigation,” Neuron 99(1), 47–55.e4 (2018). [CrossRef]
5. K. Z. Gerlei, C. M. Brown, G. Sürmeli, and M. F. Nolan, “Deep entorhinal cortex: from circuit organization to spatial cognition and memory,” Trends Neurosci. 44(11), 876–887 (2021). [CrossRef]
6. J. Lopez-Rojas, C. A. de Solis, F. Leroy, E. R. Kandel, and S. A. Siegelbaum, “A direct lateral entorhinal cortex to hippocampal CA2 circuit conveys social information required for social memory,” Neuron 110(9), 1559–1572.e4 (2022). [CrossRef]
7. F. Savelli, D. Yoganarasimha, and J. J. Knierim, “Influence of boundary removal on the spatial representations of the medial entorhinal cortex,” Hippocampus 18(12), 1270–1282 (2008). [CrossRef]
8. G. Tocker, O. Barak, and D. Derdikman, “Grid cells correlation structure suggests organized feedforward projections into superficial layers of the medial entorhinal cortex,” Hippocampus 25(12), 1599–1613 (2015). [CrossRef]
9. T. Hafting, M. Fyhn, S. Molden, M. B. Moser, and E. I. Moser, “Microstructure of a spatial map in the entorhinal cortex,” Nature 436(7052), 801–806 (2005). [CrossRef]
10. T. Solstad, C. N. Boccara, E. Kropff, M. B. Moser, and E. I. Moser, “Representation of geometric borders in the entorhinal cortex,” Science 322(5909), 1865–1868 (2008). [CrossRef]
11. E. Kropff, J. E. Carmichael, M. B. Moser, and E. I. Moser, “Speed cells in the medial entorhinal cortex,” Nature 523(7561), 419–424 (2015). [CrossRef]
12. F. Sargolini, M. Fyhn, T. Hafting, B. L. McNaughton, M. P. Witter, M. B. Moser, and E. I. Moser, “Conjunctive representation of position, direction, and velocity in entorhinal cortex,” Science 312(5774), 758–762 (2006). [CrossRef]
13. L. M. Giocomo, T. Stensola, T. Bonnevie, T. Van Cauter, M. B. Moser, and E. I. Moser, “Topography of head direction cells in medial entorhinal cortex,” Curr. Biol. 24(3), 252–262 (2014). [CrossRef]
14. L. R. Squire, C. E. Stark, and R. E. Clark, “The medial temporal lobe,” Annu. Rev. Neurosci. 27(1), 279–306 (2004). [CrossRef]
15. V. Edvardsen, A. Bicanski, and N. Burgess, “Navigating with grid and place cells in cluttered environments,” Hippocampus 30(3), 220–232 (2020). [CrossRef]
16. M. Fyhn, S. Molden, M. P. Witter, E. I. Moser, and M. B. Moser, “Spatial representation in the entorhinal cortex,” Science 305(5688), 1258–1264 (2004). [CrossRef]
17. G. Sürmeli, D. C. Marcu, C. McClure, D. L. F. Garden, H. Pastoll, and M. F. Nolan, “Molecularly defined circuitry reveals input-output segregation in deep layers of the medial entorhinal cortex,” Neuron 88(5), 1040–1053 (2015). [CrossRef]
18. J. Lu, Z. Zhang, X. Yin, Y. Tang, R. Ji, H. Chen, Y. Guang, X. Gong, Y. He, W. Zhou, H. Wang, K. Cheng, Y. Wang, X. Chen, P. Xie, and Z. V. Guo, “An entorhinal-visual cortical circuit regulates depression-like behaviors,” Mol. Psychiatry 27(9), 3807–3820 (2022). [CrossRef]
19. H. Adelsberger, O. Garaschuk, and A. Konnerth, “Cortical calcium waves in resting newborn mice,” Nat. Neurosci. 8(8), 988–990 (2005). [CrossRef]
20. W. Huang, J. Qin, C. Zhang, H. Qin, and P. Xie, “Footshock-induced activation of the claustrum-entorhinal cortical pathway in freely moving mice,” Physiol. Res. 71(5), 695–701 (2022). [CrossRef]
21. W. Denk, J. H. Strickler, and W. W. Webb, “Two-photon laser scanning fluorescence microscopy,” Science 248(4951), 73–76 (1990). [CrossRef]
22. R. Yuste and W. Denk, “Dendritic spines as basic functional units of neuronal integration,” Nature 375(6533), 682–684 (1995). [CrossRef]
23. K. K. Ghosh, L. D. Burns, E. D. Cocker, A. Nimmerjahn, Y. Ziv, A. E. Gamal, and M. J. Schnitzer, “Miniaturized integration of a fluorescence microscope,” Nat. Methods 8(10), 871–878 (2011). [CrossRef]
24. W. Zong, R. Wu, M. Li, Y. Hu, Y. Li, J. Li, H. Rong, H. Wu, Y. Xu, Y. Lu, H. Jia, M. Fan, Z. Zhou, Y. Zhang, A. Wang, L. Chen, and H. Cheng, “Fast high-resolution miniature two-photon microscopy for brain imaging in freely behaving mice,” Nat. Methods 14(7), 713–719 (2017). [CrossRef]
25. E. I. Moser, E. Kropff, and M. B. Moser, “Place cells, grid cells, and the brain's spatial representation system,” Annu. Rev. Neurosci. 31(1), 69–89 (2008). [CrossRef]
26. J. Ye, M. P. Witter, M. B. Moser, and E. I. Moser, “Entorhinal fast-spiking speed cells project to the hippocampus,” Proc. Natl. Acad. Sci. U. S. A. 115(7), E1627–E1636 (2018). [CrossRef]
27. S. Dufour and Y. De Koninck, “Optrodes for combined optogenetics and electrophysiology in live animals,” Neurophotonics 2(3), 031205 (2015). [CrossRef]
28. V. Gilja, C. A. Chestek, P. Nuyujukian, J. Foster, and K. V. Shenoy, “Autonomous head-mounted electrophysiology systems for freely behaving primates,” Curr. Opin. Neurobiol. 20(5), 676–686 (2010). [CrossRef]
29. E. Stark, T. Koos, and G. Buzsáki, “Diode probes for spatiotemporal optical control of multiple neurons in freely moving animals,” J. Neurophysiol. 108(1), 349–363 (2012). [CrossRef]
30. P. Anikeeva, A. S. Andalman, I. Witten, M. Warden, I. Goshen, L. Grosenick, L. A. Gunaydin, L. M. Frank, and K. Deisseroth, “Optetrode: a multichannel readout for optogenetic control in freely moving mice,” Nat. Neurosci. 15(1), 163–170 (2011). [CrossRef]
31. H. Qin, L. Fu, T. Jian, et al., “REM sleep-active hypothalamic neurons may contribute to hippocampal social-memory consolidation,” Neuron 110(23), 4000–4014.e6 (2022). [CrossRef]
32. L. Roux, B. Hu, R. Eichler, E. Stark, and G. Buzsáki, “Sharp wave ripples during learning stabilize the hippocampal spatial map,” Nat. Neurosci. 20(6), 845–853 (2017). [CrossRef]
33. A. G. Leal-Junior, C. R. Díaz, C. Marques, A. Frizera, and M. J. Pontes, “Analysis of viscoelastic properties influence on strain and temperature responses of Fabry-Perot cavities based on UV-curable resins,” Opt. Laser Eng. 120, 105743 (2019). [CrossRef]
34. T. Weichhart, O. Brandt, C. Lassnig, M. Müller, W. H. Hörl, G. Stingl, and M. D. Säemann, “The anti-inflammatory potency of dexamethasone is determined by the route of application in vivo,” Immunol. Lett. 129(1), 50–52 (2010). [CrossRef]
35. M. Fyhn, T. Hafting, M. P. Witter, E. I. Moser, and M. B. Moser, “Grid cells in mice,” Hippocampus 18(12), 1230–1238 (2008). [CrossRef]
36. M. K. Meffert, J. M. Chang, B. J. Wiltgen, M. S. Fanselow, and D. Baltimore, “NF-kappa B functions in synaptic signaling and behavior,” Nat. Neurosci. 6(10), 1072–1078 (2003). [CrossRef]
37. N. Schmitzer-Torbert, J. Jackson, D. Henze, K. Harris, and A. D. Redish, “Quantitative measures of cluster quality for use in extracellular recordings,” Neuroscience 131(1), 1–11 (2005). [CrossRef]
38. N. M. van Strien, N. L. Cappaert, and M. P. Witter, “The anatomy of memory: an interactive overview of the parahippocampal-hippocampal network,” Nat. Rev. Neurosci. 10(4), 272–282 (2009). [CrossRef]
39. A. Rozov, M. Rannap, F. Lorenz, A. Nasretdinov, A. Draguhn, and A. V. Egorov, “Processing of hippocampal network activity in the receiver network of the medial entorhinal cortex layer V,” J. Neurosci. 40(44), 8413–8425 (2020). [CrossRef]
40. C. Gavériaux-Ruff and B. L. Kieffer, “Conditional gene targeting in the mouse nervous system: Insights into brain function and diseases,” Pharmacol. Ther. 113(3), 619–634 (2007). [CrossRef]
41. H. Qin, J. Lu, W. Jin, X. Chen, and L. Fu, “Multichannel fiber photometry for mapping axonal terminal activity in a restricted brain region in freely moving mice,” Neurophotonics 6(03), 035011 (2019). [CrossRef]
42. L. A. Gunaydin, L. Grosenick, J. C. Finkelstein, I. V. Kauvar, L. E. Fenno, A. Adhikari, S. Lammel, J. J. Mirzabekov, R. D. Airan, K. A. Zalocusky, K. M. Tye, P. Anikeeva, R. C. Malenka, and K. Deisseroth, “Natural neural projection dynamics underlying social behavior,” Cell 157(7), 1535–1551 (2014). [CrossRef]
43. C. Montinaro, M. Pisanello, M. Bianco, B. Spagnolo, F. Pisano, A. Balena, F. De Nuccio, D. D. Lofrumento, T. Verri, M. De Vittorio, and F. Pisanello, “Influence of the anatomical features of different brain regions on the spatial localization of fiber photometry signals,” Biomed. Opt. Express 12(10), 6081–6094 (2021). [CrossRef]
44. Y. F. Chien, J. Y. Lin, P. T. Yeh, K. J. Hsu, Y. H. Tsai, S. K. Chen, and S. W. Chu, “Dual GRIN lens two-photon endoscopy for high-speed volumetric and deep brain imaging,” Biomed. Opt. Express 12(1), 162–172 (2021). [CrossRef]
45. W. Zong, R. Wu, S. Chen, J. Wu, H. Wang, Z. Zhao, G. Chen, R. Tu, D. Wu, Y. Hu, Y. Xu, Y. Wang, Z. Duan, H. Wu, Y. Zhang, J. Zhang, A. Wang, L. Chen, and H. Cheng, “Miniature two-photon microscopy for enlarged field-of-view, multi-plane and long-term brain imaging,” Nat. Methods 18(1), 46–49 (2021). [CrossRef]
46. W. Zong, H. A. Obenhaus, E. R. Skytøen, H. Eneqvist, N. L. de Jong, R. Vale, M. R. Jorge, M. B. Moser, and E. I. Moser, “Large-scale two-photon calcium imaging in freely moving mice,” Cell 185(7), 1240–1256.e30 (2022). [CrossRef]
47. T. J. Viney, M. Salib, A. Joshi, G. Unal, N. Berry, and P. Somogyi, “Shared rhythmic subcortical GABAergic input to the entorhinal cortex and presubiculum,” eLife 7, e34395 (2018). [CrossRef]
