Parvalbumin-positive neurons in the medial septum participate in the formation of hippocampal-dependent spatial memory

Domain: biology
Published: 2026-03-20
Editorial outcome: major_revision

Abstract
GABAergic medial septal neurons play a key role in regulating hippocampus-dependent spatial memory, but the underlying mechanisms are not yet fully understood. In male mice, we establish an object-place recognition impairment model using an acute sleep deprivation protocol. Here we show that parvalbumin-positive neurons in the medial septum regulate object-location memory specificity during encoding by modulating hippocampal place cell population activity. Using in vivo electrophysiology combined with optogenetics, we characterize the role of medial septal parvalbumin-positive neurons in object-place recognition during memory encoding. Preference for objects relocated to novel positions parallels directional shifts in place fields of remapping neuronal populations, as well as decreased co-activity among place cells in dorsal CA1 during the encoding phase. Activating medial septal parvalbumin-positive neurons during memory encoding rescues object-place recognition impairments. These findings suggest that parvalbumin-positive neurons in the medial septum play a causal role in object-place recognition memory.

Introduction
The medial septum (MS) is a key component of the septo-hippocampal network1. Inhibition of the MS impairs recognition of object-place association2 and disrupts the periodic activity of grid cells and temporarily alters the spatial configuration of place fields, indicating its key role in hippocampal physiology and function3. The projections from the MS to the hippocampus predominantly include cholinergic, GABAergic, and glutamatergic neurons4. While lesions to cholinergic neurons in the MS before training do not directly affect spatial learning or novel object recognition5, selective destruction of GABAergic neurons weakens working memory in a delayed-matching water maze task6. GABAergic neurons not only exhibit strong phase coupling with hippocampal oscillations but also enhance hippocampal theta power during running episodes through the septo-hippocampal GABAergic projection7, 8. Evidence shows that GABAergic neurons in the MS both project to and receive input from GABAergic neurons in the hippocampus, creating a reciprocal connection between the two regions1. This indicates the presence of direct network synchronization and signal transmission within the septo-hippocampal GABAergic network, providing a neurobiological basis for the joint involvement of the MS and hippocampus in spatial memory. The GABAergic neurons projecting from the MS are predominantly parvalbumin-positive (PV+)9, 10. Rhythmic activation of MS PV+ neurons leads to rhythmic disinhibition of hippocampal pyramidal neurons, facilitating synchronization between hippocampal and MS networks11, 12. Activation of PV+ neurons in the MS regulates theta oscillation frequency in the hippocampus and enhances spatial cognition13. Though MS PV+ neurons are involved in spatial exploration7, how these neurons contribute to spatial memory remains unclear.

The hippocampus is vital for memory and navigation, mapping self-location within the proximal environment14, 15. Structural and functional disruption of the hippocampus, such as sleep deprivation (SD), impairs the encoding, consolidation, and retrieval of spatial memory16, 17, 18. The identification of place cells, which fire selectively at specific locations within their “place fields”, provides compelling evidence of the hippocampus’s critical role in cognitive navigation mechanisms19, 20. The phase and sequence of place cells within the theta cycle in the dorsal CA1(dCA1) play a crucial role in encoding spatial information in animals21. Place cell activity is influenced by various environmental cues, including objects22, 23. Animals store the distance and direction of objects in place fields and use this information from place cells to guide navigation24, 25. Studies have shown that the place fields of place cells shift in response to the movement of objects within a given arena25, 26. Meanwhile, weaker coupling of place cells leads to the emergence of new patterns in memory behaviors27. SD disrupts the remapping of place cells, which encode the arrangement of objects, and subsequently impairs performance on the OPR task28. Nevertheless, the overall functional dynamics of place cells in models with impaired short-term spatial memory updating remain poorly understood. While MS PV+ neurons are known to pace theta oscillations in the dCA113, direct evidence connecting MS PV+ neurons to hippocampal place cells in the hippocampus remains elusive.

Here, we show that MS PV+ neurons contribute to object-location associations in spatial memory tasks by regulating downstream dCA1 place cell activity. To test this, we examine the electrophysiological characteristics of MS PV+ neurons and dCA1 place cells during object exploration and their changes under SD.

Results
Acute SD decreases sleep duration and impairs OPR
We first employed a rotating-bar SD protocol, implemented in stages, to minimize anxiety and to establish a cognitive impairment model (Fig. 1a). EEG recordings revealed a reduction in total sleep time and REM sleep duration in SD mice (Fig. 1b, 1c and 1d). Additionally, the number of wake bouts increased, while rapid-eye movement (REM) sleep bouts decreased during SD (Fig. 1c). Subsequent sleep recordings showed that REM sleep duration and bouts were significantly lower than in the control group during the second hour after SD, gradually recovering thereafter (Fig. S1a and S1b). After SD, the preference score in the OPR test was significantly reduced (Fig. 1e and 1f). However, total exploration time in the sample and test phases, total locomotive distance (Fig. 1e), locomotive time and average velocity (Fig. S1c and S1d) were not affected by SD. Meanwhile, gentle-handling SD groups using a soft brush were included as a positive control (Fig. S1e). On the second day after SD, plasma cortisol levels and performed the Elevated plus maze (EPM) test showed no significant differences among the three groups (Fig. S1f-h). These results suggest that our rotating-bar SD protocol impairs hippocampus-dependent cognitive function in mice without inducing anxiety. The rotating-bar SD protocol was used in all subsequent experiments.

Decreased theta oscillations in the dCA1 during OPR test after SD
We used PV-Cre mice and the multiple-region optrodes technique (Fig S2a, S2b and S2c) to record the activity of MS PV+ neurons and dCA1 neurons during the OPR test (Fig 2a-d). The ripple rate, ripple duration, and peak normalized power during the rest phase showed no significant differences. (Fig. 2e and Fig. S2d). After SD, we observed reduced theta oscillations, particularly evident during the sample and test phases in the dCA1 (Fig. 2f). There was no significant difference in theta oscillations between exploration and non-exploration periods within the same group (Fig. S2e). No significant differences were observed in the PSD of the MS (Fig. S2f). Then, we analyzed the LFP coherence between the MS and the dCA1. We also observed decreased coherence in the SD group during the sample phase (Fig. 2g), which paralleled the OPR impairment.

Enhanced MS PV+ neuronal firing during object exploration
To rule out any effects of PV+ neuronal activation on OPR behavior, opto-tagging experiments were conducted after the OPR test (Fig. 2d). Labeled neurons were strongly synchronized with flicker stimuli (Fig. 3a), indicating robust light-evoked responses (Fig. 3b), and over 96% of ChR2-mCherry-expressing neurons were confirmed to be PV-positive (Fig. S3d). The proportion of labeled PV+ neurons was similar between the SD and control groups (Fig. S3a). The untagged MS neurons also exhibited the highest firing rates during the sample phase. However, unlike the tagged PV neurons, their activity was lowest during the test phase. (Fig. S3b). No significant differences were detected between the two groups across the four stages of the OPR test (Fig. S3c). However, the normalized Z-score firing rate of MS PV+ neurons was significantly higher during the sample (memory encoding) and test (memory retrieval) phases compared to the habituation and rest (memory consolidation) phases (Fig. 3c). Even in the SD group, the firing rate during the sample phase remained higher than that during the rest phase (Fig. 3c).

Both phases were characterized by the presence of objects, raising the possibility that MS PV+ neurons may contribute to object exploration and memory. To test this hypothesis, we split the sample and test phases into object-exploration and non-object-exploration periods, and analyzed MS PV+ neuronal activities. As expected, the firing rate of PV+ neurons during the object-exploration period was significantly higher than that during the non-object-exploration periods, in both sample and test phases (Fig. 3d). In the SD group, PV+ neurons maintained a low average firing rate during non-object-exploration periods during the sample phase, whereas no difference was observed during the test phase (Fig. 3d).

MS PV+ neurons participate in encoding object recognition
To illustrate the encoding strategies of MS PV+ neurons in object recognition, we classified neuronal responses into excited, inhibited, or non-responsive categories based on their reactions to object exploration (Fig. 3e). Based on the distribution of exploration durations, we determined a median value of 3 s and used it to define exploration periods for subsequent analyses (Fig. S4a). There were no differences in the proportions of the three types of ensemble neurons among all recorded MS neurons between the control and SD groups (Fig. 3f and 3g). The object exploration in the memory encoding phase elicited excited responses in a substantial proportion of MS PV+ neurons (17.4%), while the object exploration during the test phase induced only minor responses in a much smaller subset of neurons (3.2%) (Fig. 3h, 3i and 3l). Notably, independent subpopulations of MS PV+ neurons responded to the two objects during the sample phase (Fig. 3h and 3m). The overall proportion of neurons responding to the objects was relatively low in the SD group (Fig. 3j, 3k and 3l). The proportion of PV+ neurons among MS neurons that exhibited excited responses to object exploration was highest during the encoding phase in the control group (Fig. S4b). Raw traces suggested that the excited responses of PV+ neurons to object exploration might not be instantaneous or short-lasting (Fig. 3e). We found that most neurons showing excited responses in the 1 s window (9/11) were also classified as excited in the 3 s window (Fig. S4c and S4d). The 3 s window captured a more comprehensive population of excited MS PV+ neurons (Fig. S4d). For PV+ neurons that showed excited responses to objects, the firing rates did not differ between exploration and non-exploration periods (Fig. S4e). The three ensembles also showed no significant differences in untagged MS neurons (Fig. S3f, S3g). Unlike place cells, firing rate maps of MS PV+ neurons that responded to the objects did not exhibit spatial specificity (Fig. S4h and S4i).

To control for the potential influence of locomotor speed on MS neuronal activity, we quantified the firing-speed relationships and found no strong population-level coupling during either exploration or non-exploration periods in the sample phase (Fig. 4a, Fig. S5a and S5b). We then computed firing-speed relationships for each neuron and classified them as positively correlated, negatively correlated, or non-correlated (Fig. 4b). In the control group, a subset of neurons (10/63) showed positive speed modulation during the sample phase; however, only 3 of these neurons also exhibited excited responses during object exploration, whereas the majority (7/10) showed speed modulation without object-related responses (Fig. 4c). This dissociation suggested that locomotor speed signals and object-related excitation are largely segregated during encoding. In the control test phase and both phases of the SD group, no overlap was observed between speed-modulated and object-responsive neurons, consistent with the reduced number of excited neurons in these conditions (Fig. 4c). Together, these results indicate that MS PV+ neurons conveying locomotor speed information are not the primary contributors to object-related processing. For untagged MS neurons, overlap with excited neurons was also observed only during the control sample phase, whereas no overlap emerged in the test phase or under SD conditions (Fig. S5c). This phase-limited convergence suggests that MS activity may be preferentially engaged during the encoding stage, while MS neuron population activity may be diminished during retrieval and further weakened by SD.

To confirm the role of PV+ neurons in object memory, we quantified single-neuron discriminability between the two objects using receiver operating characteristic (ROC) analysis (Fig. S5d). In the sample phase, MS PV+ neurons in both control and SD groups showed the area under the ROC curve (AUC) values significantly above chance level (0.5), whereas untagged neurons did not, indicating that individual MS PV+ neurons could at least partially distinguish the two objects (Fig. S5g and S5h). Nonetheless, the majority of AUC values were narrowly distributed around 0.5, indicating that single-neuron discriminability was generally weak (Fig. S5e and S5f). We therefore evaluated object decoding at the population level using a Generalized Linear Model (GLM) decoder (Fig. 4d). The trained classifier for all MS neurons accurately predicted objects placed in different corners of the arena during memory encoding, but the decoder failed to distinguish the two objects after one object was moved (Fig. 4e). After SD, however, the decoder trained using all MS neurons failed to distinguish two objects during both sample and test phases (Fig. 4e). More intriguingly, the decoder constructed from opto-tagged MS PV+ neurons was able to reliably distinguish two objects in both the sample and test phases (Fig. 4f), but this discriminative capacity was lost in the test phase after SD, aligning with our behavioral results (Fig. 4f). For the untagged MS neurons, the decoder failed to predict object identity during the test phase in the control group but unexpectedly succeeded in both phases after SD (Fig. 4g). Based on single-neuron ROC and GLM population decoding, individual MS PV+ neurons carry limited information, but their combined activity reliably distinguishes the two objects, highlighting population-level encoding of object identity during the OPR task. However, their role in encoding the object-spatial associated information, especially spatial memory updates, remains unclear and warrants further investigation.

SD leads to a disorganized reorientation of place fields and increased co-activity of the place cell population
Hippocampal cells code environmental changes through increases or decreases in firing rate, a phenomenon known as rate remapping29, which reflects the dynamic nature of spatial representations30. The proportion of place cells recorded in the dCA1 showed no difference between the control group and SD group (Fig. S6a). Place cells in both groups exhibited similar firing patterns, with increased firing rates during the sample and test phases (Fig. S6b). We first analyzed the place field distributions across different phases of the OPR task. Across both control and SD groups, during both sample and test phases, place fields exhibited non-uniform distributions (Fig. S6c and S6d). As a positive control, we performed an OPR test without object movement and recorded from dCA1 (Fig. S7a). The unmoved group also showed a non-uniform distribution of place fields during the sample phase (Fig. S7e). Across all three groups, the mean vector direction of place fields was not aligned with the object locations, suggesting that place cell distributions are influenced by not only object positions but also other spatial cues such as arena boundaries and edge landmarks. We next focused on changes in place fields to assess object-related remapping. Previous studies have shown that distinct CA1 subpopulations respond differently during the OPR task28. Following the same definition28, we classified place cells as either “unstable” or “stable” depending on whether they exhibited remapping between the habituation and sample phases (Fig. 5a). We did not detect significant changes in the proportion of unstable cells between the control and SD groups (Fig. 5c), indicating that the majority of place cells exhibited remapping during the sample phase of the OPR test.

As previous studies suggest that stable cells may encode contextual information28, we focused on remapping place cells. Hippocampal cells respond to object cues and are thus possibly involved in forming object-place associations31. We first examined whether place fields shifted toward the new location of the moved object. The proportion of cells showing such shifts was significantly higher in the control group than in the SD group (Fig. 5b and 5c). To further examine the overall pattern of place field changes before and after object movement, we quantified the change in place field distance during the test phase relative to the sample phase. The results showed that place fields in the control group shifted significantly closer to the moved object compared to the SD group, and this effect was primarily driven by changes along the X-axis (Fig. 5d). Following object movement, place field shifts in the control group were non-uniformly distributed and biased toward the moved object (Fig. 5e), whereas shifts in the SD group were uniformly distributed (Fig. 5f). As for the unmoved group, 36.9% of place cells maintained stable place fields during the habituation phase on the second day (Fig. S7b and S7c). Moreover, compared to the object-moved condition, the no-movement test phase showed higher Pearson correlation coefficients between place fields in the test and sample phases (Fig. S7d). In the test phase, the distribution of place field vector shifts remained uniform, indicating no preferential direction (Fig. S7f).

We next asked how the firing activity of place cell populations contributes to object-location memory. The hippocampal network also relies on the collective activity of its neuron populations to support object-location memory27. We quantified the co-activity of each place cell pair (i, j) in a mouse by predicting the spike discharge of neuron j based on the activity of neuron i, while controlling for the activity of the rest of the neuronal population (Fig. 5g). This procedure generated matrices that revealed the pairwise coactivity structure of the neuronal population during object exploration (Fig. 5h). Higher clustering coefficients were observed in the SD group during the memory encoding phase, indicating that SD constrained place cells within a highly correlated activity space, thereby impairing subsequent object-location memory (Fig. 5i and 5j). Unlike the control group, the unmoved OPR test included an additional 10 min habituation2 period prior to the sample phase (Fig. S7a), allowing the mice to become more familiar with the environment of the arena, including the cues. Compared to the control group, the co-activity of place cells significantly increased, though it remained notably lower than that of the SD group (Fig. S7g). However, no differences were observed during the test phase (Fig. S7h). When comparing the changes in co-activity coefficients of the same neurons between the sample and test phases, the change of individual neurons in the unmoved group was smaller than in the control group (Fig. S7i), suggesting that the co-activity of place cells was more stable when the objects were not moved.

MS PV+ neurons exhibiting excited responses to object exploration showed coherent activity with place cells
Previous studies primarily focused on how MS PV+ neurons regulate hippocampal theta oscillations. Our analysis indicated that the firing rates of MS PV+ neurons and place cells displayed similar trends during the OPR test (Fig. S6b and S8a), with relatively higher activity observed during the sample and test phases. To further test the correlation at the neuronal level, we first measured the pairwise correlations of the activity of MS PV+ neurons and place cells during object exploration (Fig. 6c and S8b). The number of correlated pairs with place cells during the memory encoding phase was greater for MS PV+ neurons exhibiting excited responses to objects compared to those that did not respond (Fig. 6a and 6b). Notably, the place cells correlated with different MS PV+ neurons were not the same neuronal ensemble (Fig. 6d). In the SD group, MS PV+ neurons that exhibited excited responses to object exploration showed no difference in correlated pairs with place cells compared to non-responsive neurons during the sample and test phases (Fig. 6b). In the control group, during the memory encoding phase, all MS PV+ neurons exhibiting excited responses to object exploration were paired with place cells (Fig. 6e). To further examine the temporal lead-lag relationships between paired neurons during the sample phase, we computed cross-correlations and quantified the positive and negative lags (Fig S8c). Among pairs in which PV neurons exhibited excited responses to the object, 2 pairs showed short lags < 5 ms, and the remaining 8/18 pairs were directed from MS PV+ neurons to dCA1 place cells. However, most non-responsive PV+ pairs exhibited longer lags, typically from place cells to PV+ neurons (15/20) (Fig S8d). These results may provide insight into the potential direction of information flow underlying object-spatial associative encoding.

Optogenetic activation of MS PV+ neurons during memory encoding regulates the activity pattern of place cell populations
Long projections of MS PV+ neurons act as a theta pacemaker for the dCA14. Based on the average firing rate of MS PV+ neurons during the OPR test, we selected a 12 Hz frequency, within the theta band and above 10 Hz, to activate MS PV+ neurons. Activation of PV+ neurons during the sample phase effectively mitigated the reduction in theta oscillations observed during both the sample and test phases after SD (Fig. 7a and S9e). However, activating PV+ neurons during the rest and test phases did not improve theta oscillations in these two phases (Fig. S9a and S9b). Similarly, activation of PV+ neurons during the sample phase improved the decreased coherence between the MS and CA1 in the theta band observed during the sample and rest phases after SD (Fig. 7b). However, activating PV+ neurons during the other two phases did not show a restorative effect (Fig. S9c and S9d). Activating MS PV+ neurons had no effect on ripple rate during memory consolidation (Fig. S9f).

We stimulated MS PV+ neurons in the SD group during three phases of the OPR test and calculated the normalized firing rates of place cells. A large proportion of neurons (38.2% of place cells) showed excited responses to laser stimuli during memory encoding, with no inhibited ensemble cells observed (Fig. 7c). However, during memory consolidation and retrieval, the proportion of excited ensemble cells decreased, and a significant number of place cells remained inactive throughout the analysis window (Fig. 7b). Activation of MS PV+ neurons during memory encoding significantly increased the proportion of place cells with fields relocated near the displaced objects compared with the SD group, restoring it to levels comparable with the control group (Fig. 7d and S9g). This effect was accompanied by an increase in the total displacement of place fields, particularly along the X-axis (Fig. 7e and S9h). Moreover, place field shift directions showed a non-uniform distribution, with a bias toward the direction of object movement (Fig. 7f). In contrast, activation during the rest or test phase did not produce these effects (Fig. 7d, 7e, 7g, 7h, S9g and S9h).

Furthermore, the activation of MS PV+ neurons during memory encoding was found to relax the constraints on place cells co-activity to accommodate spatial memory (Fig. 7i). Activating MS PV+ neurons during the other two phases of the OPR test increased the co-activity of place cells during the memory retrieval (Fig. 7j). We also analyzed the spatial information of place cells, and found no significant differences among the groups (Fig. S9i), indicating similar place cell properties per se. Finally, to determine whether the shift of the place field and the co-activity of place cells were associated with preference scores in the OPR test, we calculated correlations between these measures. In all cases, we observed positive correlations across all three indicators (Fig. 7k). These results suggest that MS PV+ neurons can modulate the activity and the function of place cells in the dCA1 during memory encoding.

Activation/inhibition of PV+ neurons in MS bidirectionally modulates OPR behaviors
Based on the above findings, we hypothesized that activating MS PV+ neurons during the sample phase would rescue memory impairment induced by SD. We injected a Cre-dependent virus and implanted an optic fiber in the MS of PV-cre mice (Fig. 8a and 8b). Activating MS PV+ neurons during the sample phase restored the decreased preference score caused by SD, without affecting the total exploration time or the distance traveled by the mice (Fig. 8c). However, activating MS PV+ neurons during the rest or test phases failed to show such effect (Fig. S10a and S10b). Inhibiting PV+ neurons during all three phases abolished the increased preference for an object in a novel position and reduced the total exploration time during the test phase, compared to Laser-only controls (Fig. 8d, S10c and S10d).

To further confirm the role of the MS-dCA1 pathway, we photostimulated ChR2-expressing MS terminals in the dCA1 (Fig. 8e). Activating MS PV+ projections to the dCA1 effectively rescued the OPR impairment induced by SD (Fig. 8f). In contrast, inhibiting MS projections to the dCA1 led to OPR task impairment (Fig. 8g), without affecting other dimensions (Fig. S10e and S10f). All above data support that the projection of PV+ neurons from the MS to the dCA1 is crucial for mitigating cognitive impairment caused by acute SD.

Methods
Animals
Adult male C57BL/6 mice (20 -35 g, age 8 -12 weeks) were provided by Department of Laboratory Animal Sciences, Peking University. Adult male parvalbumin-Cre transgenic mice (26 -35 g, age 8 -12 weeks) were obtained from Jackson Laboratories. All experimental animals were housed in plastic cages at a controlled temperature (25 ± 2 °C) under a 12 h light/dark cycle with standard laboratory chow and tap water available ad libitum. All experimental procedures were conducted under approval from the Animal Care Committee of the Faculty of Basic Medicine, Peking University (approval number: DLASBD0099), following the guidelines of the China Council on Animal Care.

SD protocol
An automated SD cylindrical apparatus (Anhui Yaokun Biotechnology, Anhui, China) with a rotating bar was used for all SD procedures. Mice were acclimated to the apparatus for at least 5 h per day for 3 consecutive days, with fresh bedding, food, and water provided. To induce SD, the bar was rotated for 1-2 min every 10 -15 min by a motor at approximately 3 rpm, with random reversals in rotational direction to prevent the mice from adapting to the rotation pattern. Mice in the experimental groups underwent 5 h of SD in the apparatus, while control animals were housed in identical apparatuses but allowed to sleep. For the gentle handing SD groups, the method of awakening was replaced with gentle manual stimulation using a soft brush, while all other protocol parameters remained unchanged.

OPR procedure and behavioral analysis
The test area consisted of a 40 cm3 open-topped square container. The bottom and walls of the container were painted with blue waterproof paint and unique visual cues were randomly present on the walls of the square container. Before the test phase, mice were adapted in the square container and rest box for 20 min per day for 2 days. On the first day of testing (test area habituation/baseline), mice were placed in the empty test area and allowed to explore freely for 10 min before being returned to their homecage. For the second day, mice were placed in the container with two identical objects (two cubes, arbitrarily named as OA and OB) at two different corners in the sample phase. Mice were allowed to explore freely for 10 min, then the container and objects were cleaned with 75% ethanol. In the rest phase, mice were placed into the rest box for 15 min. Pseudorandomly, one of the two objects (OA) remained unchanged and left in the same place, with the other (OB) moved to a new corner. For the test phase, mice were placed into the test area again and allowed to explore for 10 min. Once the mouse headed to and contacted the object by its nose, one exploration was counted. The time of object exploration was recorded (Gene&I, Shanghai, China). Mice with an object preference score greater than 70% or less than 30% during the probe phase were excluded to avoid object bias. The preference score was calculated as the exploring time OB / (OB + OA) in the test phase.

EEG/EMG surgery and sleep state analysis
Prefabricated 2-channel EEG headmounts (Pinnacle Technology) were implanted at the following coordinates from Bregma (in mm): frontal leads (AP: +3, ML: ± 1) and parietal leads (AP: -2, ML: ± 1). The headmounts were secured with cyanoacrylate and dental cement. Screws were placed in the bone above the frontal cortex and cerebellum to serve as ground and reference, respectively. An electromyograph (EMG) channel was established by inserting two wires into the nuchal musculature, which were secured with VetBond. Dental cement was applied to permanently fix the implant to the skull. EEG/EMG signals were recorded for 12 hours following the SD period (Pinnacle Technology, Lawrence, KS). The headmounts were connected to a preamplifier for first-stage amplification (100 ×) and initial high-pass filtering (0.5 Hz for EEG and 10 Hz for EMG). Signals were sampled at 1000 Hz and digitized using Sirenia Acquisition software (Pinnacle Technology). Animals with excessive noise in any channels (defined as >10% of epochs classified as artifact) were excluded from further analysis. The EDF files were exported to MATLAB and then analysis sleep state using an open source MATLAB toolbox (https://data.mendeley.com/datasets/c872f83pdz/4)58

Plasma corticosterone measures
Plasma samples were collected approximately 12-15 hours after SD, including from the gentle handling SD group. The samples were centrifuged, aliquoted, and immediately stored at -80 °C. Corticosterone concentrations were determined using a commercial ELISA kit (SEKM-0273, Solarbio Science & Technology Co., Ltd.), according to the manufacturer’s instructions.

The Elevated Plus Maze (EPM) Test
The EPM comprised four arms (30 × 5 cm), arranged in a plus shape with two open and two closed arms positioned perpendicularly. Closed arms had 15 cm high walls, while open arms lacked walls but featured 5 mm borders to prevent falls. The maze was elevated 40 cm above the ground and placed under dim lighting. Each mouse was placed in the central zone facing an open arm, and behavior was recorded for 5 min via an overhead camera. After each trial, the maze was cleaned with 75% ethanol and dried. Recorded parameters included the number of full entries into open and closed arms (all four paws), time spent in open arms, and total distance traveled. Movements were tracked and analyzed using ezTrack59 and MATLAB 2024a software.

Electrode implantation and stereotaxic injection
Mice were anesthetized using isoflurane gas (~2.0%), and surgeries were performed using stereotaxic apparatuses (RWD, Shenzhen, China). The scalp was sterilized with iodophors and 75% ethanol, in turn, and incised along the skull midline. The Cre-dependent viral vector (AAV9-EF1α-dio-ChR2-mCherry or the AAV9-EF1α-dio-mCherry, 0.3 μl) was injected into MS (AP: +1 mm; ML: -0.75 mm from bregma; DV: -4.36 mm from brain surface, angled 10 degrees medially), using an automatic microinjection system (World Precision Instruments, USA), at a rate of 60 nl/min. The needle syringe was left in place for 10 min before withdrawal for solution diffusion. The skin was sutured by absorbable sutures and tissue adhesive (3M vetbond, Hong Kong), and then sterilized with iodophors and 75 % ethanol. Further experiments were performed 3 weeks after virus injection.

After recovery for 3 weeks, animals were implanted microdrive, which contained four recording tetrodes (tungsten wires, 20 μm in diameter, California Fine Wires Company), aimed at the right dCA1(AP: -1.95 mm ML:1.35 mm DV: -1.2 mm) and an optrode aimed at the MS area (AP: 1.0 mm ML: -0.2 mm DV: -4.5 mm). For ‘optrode’ (optic fiber + tetrode), four tetrodes were cemented to a 100 mm core optic fiber (Inper, Hangzhou, China), with the tetrode tip extending 0.5 mm beyond the tip of the optic fiber. The impedance of each tetrode was between 1.0 to 1.5 MΩ. Two screws, soldered with the silver wires, were positioned to the cerebellum was used as a recording ground and reference. Implants were secured to the skull using dental cement (Shanghai New Century Dental Materials Co., Shanghai, China), and protected by a custom, 3D-printed shell for the longitudinal recording. Mice were allowed for at least 1 week of recovery prior to behavioral testing.

Optogenetic
Viral vectors were infused as described above 1-2 weeks before optical fibers. optic fibers (200 μm in diameter, Inper, Hangzhou, China) were implanted 0.20 to 0.50 mm above MS (AP: +1 mm; ML: -0.75 mm; DV: -4.3 mm, angled 10 degrees medially). Viral vectors were used to express either channelrhodopsin-2 (ChR2) or halorhodopsin (NphR). AAV9-dio-mCherry was used as the control fluorescent protein.

Optic fibers were implanted to selectively stimulate the PV+ neurons in MS and the axonal terminals of PV+ neurons in the dCA1. An optogenetic system (Newdoon Inc., Hangzhou, China) was used to deliver 5 ms pulses of blue light (470 nm) at 12 Hz with a power of 5 mW at the tip of the optical fibers, or orange light (589 nm) at 20 mW, delivered intermittently with 1 min pauses every 2 min. In the behavioral experiments, mice received activation/inhibition stimulation for 10 min during the sample, rest, and test phases according to the above parameters.

In vivo electrophysiology
Data acquisition and processing.
After 1 week recovery from implantation, mice were handled and habituated to the experimenter and the testing environment for at least 3 days prior to recording. During the habituation, tetrodes were gradually lowered to the CA1 pyramidal cell layers by rotating a thumb screw on the microdrive. Recordings were conducted as animals undergoing the OPR task as described above. Video recordings of the whole process of the OPR task were captured using an overhead camera. Movements were tracked and analyzed using ezTrack59 and MATLAB 2024a software.

A recording headstage and SPI cables were connected to a multichannel extracellular recording system (Intan Technologies) via a standard USB interface board60. A pulley system was designed to counterbalance the weight of the cables and headstage, ensuring minimal strain on the animals during recordings.

The recorded data were preprocessed by a 300-Hz high-pass filter. Spikes were initially detected via thresholding to include signals exceeding 5 standard deviations above the common average noise levels and were further sorted automatically using KlustaKwik (http://klustakwik.sourceforge.net/) followed by manual sorting with the software Klusters61. Units with poor signal-to-noise ratio, and those that did not have a clear refractory period (a large central notch in the autocorrelogram), were excluded. Units were automatically classified into two categories: putative pyramidal cells and putative interneurons using CellExplorer toolbox62, based on the peak widths of spike waveforms and autocorrelograms. CellExplorer automatically identifies putative narrow spike-width interneurons with trough-to-peak latency ≤ 0.425 ms, wide spike-width interneurons with trough-to-peak latency > 0.425 ms. The raw data were down-sampled to 1250 Hz, and low-pass filtered at 200 Hz to extract LFPs. LFP-related analysis (PSD and coherence) was averaged from each channel or each channel pair.

LFP Analysis
The PSD of the local field potentials LFPs was calculated from channels that exhibited clear pyramidal neuron waveforms using a multitaper estimation method, implemented via the function mtspectrumsegc.m from the Chronux toolbox63. The PSD was then divided into five frequency bands: delta (1-4 Hz), theta (4-12 Hz), beta (12-30 Hz), low gamma (30-50 Hz), and high gamma (50-100 Hz). Coherence was also computed using a multitaper method, utilizing the function coherencysegc.m from the Chronux toolbox63. To minimize the influence of electrode position on LFP recordings, mice were included in the analysis only when precise electrode placement was confirmed in both brain regions, with ≥20 place cells in the dCA1 and ≥10 neurons in the MS.

For ripple detection, the channel with the highest mean power in the ripple frequency band (100-250 Hz) was selected. The Hilbert amplitude was then calculated. Putative ripples were identified at time points exceeding 2 standard deviations (s.d.), and those without peaks exceeding 3 s.d. were discarded. Ripples with intervals shorter than 30 ms were merged, while those with a duration shorter than 20 ms or longer than 100 ms were excluded. All detected ripples were used to calculate their individual durations and peak normalized powers. The duration of each ripple was defined as the time during which the normalized ripple power remained above the low threshold. The ripple power was defined as the peak normalized power (in s.d. above the mean) within each detected event. For group analysis, mean duration and power were calculated per mouse and used for statistical comparisons.

To quantify the time-frequency dynamics of dCA1 activity during laser stimulation, we computed spectrograms using the short-time Fourier transform (STFT). Power spectra were calculated for the period from 1 s before to 1 s after laser onset.

Testing optogenetic response
Optogenetic tagging experiments were performed on PV-Cre mice. The optogenetic stimulation lasts for 1 minute at a frequency of 12 Hz (720 trials). Cells expressing Cre were identified using the Zeta-test64, 65. The Zeta test is a recently developed parameter-free statistical test that can be used to determine whether neurons show a time-dependent modulation of their firing rates by an event. The Zeta test was applied to the period around the laser-onset (-10 ms, 10 ms) to test which neurons showed significantly modulated spiking activity (p < 0.05). Cells were classified as optogenetically tagged if they exhibited significant modulation and if the first crossing of the peak half-height occurred within 10 ms of laser onset. To prevent misclassification due to laser artifacts, neurons with peaks occurring earlier than 1 ms after the onset of optogenetic stimulation were excluded from analysis. To further ensure PV+ specificity, wide spike-width labeled neurons were excluded from subsequent analyses (Fig. S3e).

Neuronal response
To calculate the mean firing rate of the PV+ neuron in the OPR test, the mean firing rate in 4 phases was normalized by using the Z-score to the baseline (i.e., by dividing the baseline-subtracted value by the standard deviation within the pre-interval; habituation period). We compared the mean firing rate of neurons in different phases using a paired-sample t-test.

To identify PV+ neurons whose firing rates were modulated by object exploration, we calculate the mean firing rate in exploration time and non-exploration time in the sample phase and test phase, respectively. We compared the mean firing rate of neurons in different phases using a paired-sample t-test.

To reveal stimulus responses of PV+ to object exploration, tagging neuron was binned in 100 ms epochs normalized to the baseline using a Z-score function. We segmented the data using a window analysis time of 4s (-1 s to 3 s/1 s with respect to exploration beginning time). We designated any neurons for which the normalized firing rates <1.0 and p < 0.05 as being significantly inhibited while the normalized firing rates >1.0 and p < 0.05 as being significantly activated by a given stimulus. Non-responsive neurons were defined when p > 0.05.

To measure the responses of place cells to PV+ stimulation in the MS, mice in SD groups received continuous 10 min blue-light stimulation during the sample, rest, and test phases, as described in the previous optogenetics section. All spikes were binned into 200 ms, and the mean firing rate was normalized using the Z-score. A paired t-test was conducted to compare the firing rates 20 s before and 20 s after laser onset. Responses were classified as excited (increased firing rate with p < 0.05), inhibited (decreased firing rate with p < 0.05), or neutral (p > 0.05). Cells with a consistent firing rate of 0 Hz were defined as non-firing cells.

Relationship between neuronal activity and locomotor speed
Mouse locomotion speed was calculated based on the distance traveled per unit time (per second). PV+ neuronal firing rates were binned into 1s epochs, and a scatter plot was generated to visualize the relationship between velocity and firing rate for each bin. Linear regression analysis was performed to fit the data, and the correlation coefficient (R value) was computed to quantify the strength of the association. For each neuron, the animal’s running speed and the corresponding firing rate were also binned in 1 s. A Spearman rank correlation coefficient was then computed between speed and firing rate for each neuron. Neurons were classified as positively modulated (r > 0, p < 0.05), negatively modulated (r < 0, p < 0.05), or not significantly correlated with running speed (p > 0.05).

Single-neuron ROC analysis
To quantify the discriminability of individual MS neurons between objects, we performed a ROC analysis. For each neuron, the input feature was the trial-by-trial response, quantified as a measure of response in the binned spike counts (100 ms bins) of each PV+ neuron recorded within a 3s window following the exploration of objects A or B. Using MATLAB’s built-in ‘perfcurve’ function, we constructed the ROC curve by varying a threshold across the distribution of neuronal responses, and computed the true positive rate (proportion of trials correctly classified as one object) versus the false positive rate (proportion of trials incorrectly classified). The area under the ROC curve (AUC) was then calculated as a summary metric of discriminability: an AUC approaching 1 indicates a strong ability of the neuron to distinguish between objects, while an AUC near 0.5 indicates chance-level discrimination.

Object neural decoding
A GLM with a logistic regression link function was trained to classify each object explored during the OPR task. The input features for decoding were the binned firing rates (100 ms bins), Z-score normalized, of each PV+ neuron recorded within a 3 s window following the exploration of objects A and B. For each trial, the resulting data matrix (30 bins × N neurons, where N is the total number of recorded cells for the All-cells decoder) was concatenated across object A and B explorations.

Exploration trials were randomly divided into training (70%) and testing (30%) sets. The GLM decoder was implemented using the fitglm function from MATLAB’s Statistics and Machine Learning Toolbox. Decoder performance was evaluated by applying the predict function to the test data. To assess the statistical significance of the decoder, we performed a permutation test by shuffling the correspondence between the training data and their object labels (1,000 iterations). For each shuffle, the GLM was retrained using the same model structure and cross-validation procedure, and decoding accuracy was calculated on the test set. This produced a null distribution of accuracies expected by chance. The actual decoding accuracy was then compared to this null distribution, with a significance threshold set at p < 0.05.

Place cells Analysis
The position of the animals was tracked using ezTrack software59. The position and spiking data were binned into 1 cm × 1 cm grids to generate raw spike count and occupancy maps. Cells with peak firing frequencies below 1 Hz were excluded from the analysis. The raw maps were then smoothed using a 4 cm Gaussian kernel, and a smoothed activity map was created by dividing the smoothed spike map by the smoothed occupancy map. Only periods where the animals had instantaneous running speeds of 1 cm/s or more were included in the analysis.

A place field was defined as a contiguous region of at least 8 bins where the firing rate exceeded 60% of the peak firing rate within the maze, and contained at least one bin where the firing rate was above 80% of the peak66. For each cell, the spatial information score (bits per spike) was calculated from the smoothed maps using the established formula67. Units with information content lower than 0.3 bits/spike were not considered as place cells.

The remapping value was estimated by averaging the Pearson R correlation values calculated from the spatial firing rate maps of individual cells between phases. Cell types in the sample phase were classified into two categories depending on whether they remapped (unstable cells), or remained stable (stable cells) compared with the habitation phase, with stability defined as the correlation value above 0.35 as a threshold28.

Analysis of place field displacement and vector shifts
For each place cell, the centroid of its firing field was calculated from the two-dimensional firing rate map. The geometric center of the arena was defined as the origin (0,0), and each centroid was converted to polar coordinates (r, θ) to examine whether the spatial distribution of place fields was uniform. The Euclidean distance from each place field centroid to the two object centers was also measured.

We then quantified the vector direction and mean distance of place field shifts for each place cell before and after object movement. For neurons with multiple place fields, the average change in distance for each place field was computed. Displacement along the x and y-axes was extracted from the vector components, with positive values indicating movement toward the new object location during the test phase, and negative values indicating movement away from it. Place cells that underwent remapping were then classified accordingly. To analyze the directional distribution of place field shifts, the center of each place field during the sample phase was set as the origin. The displacement of the place field in the test phase relative to this origin was calculated, resulting in a two-dimensional offset vector (x, y). This Cartesian displacement was then converted into polar coordinates to obtain both the direction (θ) and magnitude (r) of the shift. To determine the overall directional trend, the mean vector length was computed based on a distance-weighted average of the displacement directions across all cells. Finally, to assess whether the distribution of place field and shift directions deviated from circular uniformity, we applied Watson’s U2 test. The statistic was calculated in its discrete form as follows.
U2 = ∑n (Fi - Ui)2 + 1 - n( 1 ∑n (Fi - Ui))2 (1)
i=1 i i 12n n i=1 i i
where n is the sample size, Fi=(i-0.5)/n is the empirical cumulative distribution function of the sorted sample angles (rescaled to [0,1]), and Ui=αi/(2π) is the theoretical cumulative distribution corresponding to the i-th observed angle αi (in radians). Watson’s U2 test was chosen because it is an omnibus test that does not assume unimodality and is more robust for detecting departures from uniformity in multimodal or irregular circular distributions.

Clustering coefficient of Neuronal coactivity
We constructed hippocampal population graphs to represent the coactivity relationships between the spike trains of all recorded place cells during object exploration27. For any two place cells from a given mouse (i, j), we used the regression coefficient β obtained by training a Generalized Linear Model (GLM):
xj ~ βij xi + αij P (2)
where xj, xi are the z-scored firing rate of individual neurons j (the target) and i (the predictor), and P is the summed activity of the other N-2 neurons, with αij weighting the influence of the population contribution to the activity of target neuron j.
P = ∑ N n=0 {i, j} xn (3)
The recorded neurons and their coactivity associations are represented as the nodes and edges, respectively, in the coactivity graph for each task session. We characterized each graph using its adjacency matrix, A, which is an N×N square matrix capturing the pairwise coactivity relationships within the network. The symmetry in the weights of the network being ensured by setting A = (A+AT)/2 (4) to form an undirected graph. Then we computed the clustering coefficient Ci to characterize the network’s local coactivity structure by scoring the triadic firing relationships established by each neuron i with the other neurons in the population, using the formula proposed by Onnela et al68:
Ci = ∑ jq (β̂ ijβ̂ iqβ̂ jq) 1/3 (5)
ki (ki-1)
where j and q are neighbors of neuron i, all edge weights are normalized by the maximum edge weight in the network β̂ = β/mean(β)(6), and ki is the degree of neuron i, which in these weighted graphs with no self-connection is equal to the number of neurons minus one.

Pearson’s R threshold comparison for defining significantly correlated pairs
To assess the coherence between MS PV+ neurons, which exhibited excited responses during object exploration, and CA1 place cells, we analyzed the pairwise activity of cell pairs69. The Z-scored activity of PV+ neurons and place cells were segmented into 50-ms time bins over a 3-second window following object exploration. Instead of using a predefined threshold, we determined the significance of Pearson's R for correlated cell pairs based on a shuffled distribution of R values. Specifically, for each neuron pair, we randomly shuffled the temporal order of the binned, z-scored firing rates (1000 iterations) and recalculated Pearson’s R after each shuffle, generating a null distribution of R values expected by chance. The empirical p-value for each neuron pair was then obtained by comparing its actual correlation coefficient with this null distribution. We then applied an R threshold corresponding to p < 0.05 from the shuffle distribution to determine the number of correlated pairs (Fig. S5A). Finally, we calculated Pearson’s correlation coefficient across all pairwise neuron comparisons. For all correlated activity analysis, only mice with ≥20 place cells in the dCA1 and ≥10 neurons in the MS were included in the analysis.

For each correlated PV+-place cell pair, we calculated the normalized cross-correlation between their binned spike trains using the ‘xcorr’ function in MATLAB (maximum lag ±10 bins). Spike trains were binned at 5 ms. A positive lag indicates that the place cell activity lagged behind the PV+ neuron. For each cross-correlogram, we extracted the peak correlation value and the time lag at this peak, which reflect the temporal lead-lag relationship.

Histology
Mice were deeply anesthetized with 1% pentobarbital sodium and transcardially perfused with 0.9% saline at room temperature, followed by ice-cold 4% paraformaldehyde (PFA, pH 7.4) in 0.1 M phosphate buffered saline (PBS). Extracted brains were post-fixed with 4% paraformaldehyde (PFA) overnight and cryoprotected in 20% and 30% sucrose solutions in turn. Brains were then frozen in O.C.T. (Sakura Finetek, Inc.) and sectioned coronally into 50 μm slices using a cryostat microtome (Model 1950, Leica Instrument Co., Ltd.) for identifying electrode tracks and staining. Microlesions were induced at the recording sites 30 min before perfusion by passing currents (6 s at 25 mA on at least five channels). Brain sections were stained with Nissl and photographed by light microscope (Leica DMI 4000B, Wetzlar, Germany).

Viral infection was validated after behavioral tests. Brain sections were mounted after incubated with a DNA specific fluorescent probe (DAPI: 4’, 6-Diamidino-2-Phenylindole, 1:1000, Cell Signaling Technology). Fluorescence images were taken by a laser scanning confocal microscope (model FV1000, 1128 Olympus Co., Ltd, Tokyo, Japan). Mice with poor viral expression or mis-targeted electrode placements were excluded from further analysis.

Immunostaining
Brain preparation was performed as described above. Fixed brains were sectioned into 30 µm slices using a cryostat (Leica CM1950) for immunostaining. Free-floating sections were rinsed in PBS, blocked for 1 h in 5% BSA/0.3% Triton X-100, and incubated with rabbit anti-parvalbumin antibody (1:500, ab11427, Abcam) at 4 °C for 24 h. After PBS washes, sections were incubated for 1 h at room temperature with Alexa Fluor 647-conjugated goat anti-rabbit IgG (1:500, ab150075, Abcam), counterstained with DAPI (1:1000, 4083S, CST), and imaged with a confocal microscope (Olympus FV1000). Cell numbers were quantified using Image-Pro Plus 6.0 (Media Cybernetics) software.

Statistics
All statistical analyses were performed in MATLAB 2024a or GraphPad Prism 10. Electrophysiology-related visualizations, including heatmaps and spike raster plots were generated using MATLAB 2024a. Electrophysiological firing rates were analyzed using Wilcoxon rank sum tests, p < 0.05 was taken as statistically significant. Chi-square analyses were used to compare proportions. Behavior data and LFP data were analyzed with two-tailed unpaired Student’s t test, multiple unpaired t test, one-way ANOVA and two-way ANOVA followed by Bonferroni post hoc multiple-comparison test. All graphs contained individual data points and means ± SEMs.

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "The study by Zheng and colleagues assesses the contribution of medial septum parvalbumin neurons to a memory deficit associated with sleep deprivation. The results show that mice deprived of sleep show an impairment in memory for a displaced object in a spontaneous object-location memory task. The sleep deprived animals showed a decreased amount of CA1 theta during the sampling of the objects and the subsequent test compared to the non-deprived mice. Medial septal parvalbumin neurons showed increased firing during the sample phase of the task compared with habituation and rest in the control animals, but not in the sleep deprived animals. In both, however, there was an increase in firing when the animals explored objects as opposed to non-object exploration periods. PV cells also show strong discrimination between the stimuli during the sample phase of the task, though less so in the test phase. So the assumption is that PV cells encode object identities and that decreased in MS PV cell activity causes the deficit in memory for a displaced object. Stimulation of PV cells resulted in a greater number of hippocampal cells with fields that followed the objects, and the proportion of object following cells correlated highly with the animal’s preference for the displaced object. Finally, stimulating PV neurons appeared to ameliorate the sleep-induced impairment in preference for the displaced object.\n\nThe analysis requires more detail. For example, the authors claim that there is a decrease in EEG coherence between the medial septum and CA1 during the sample phase for the theta rhythm in sleep deprived mice compared with non-sleep deprived mice. However, no interaction term is reported from the ANOVAs. Without a significant interaction between condition (Sleep x EEG rhythm), post-hoc tests are not justified. The authors should report the results of the ANOVAs (and apologies to the authors if these results are buried in the supplementary material somewhere) and delete claims if interaction terms do not allow post-hoc tests.\n\nThe authors argue that a smaller percentage of PV cells are active in the sleep deprived animals during the sample period, but it appears that the opposite is true during the test period.\n\nThe authors should correct the statement that the PV neurons fired at a higher rate during the sample and test compared to habituation (correct) and rest (slightly incorrect – the test did not differ significantly from the preceding rest – at least according to Figure 2I.\n\nThe analysis from the decoder (e.g., Figure 3) is a little puzzling. The authors claim that the PV neurons in sleep deprived mice show a reduced discriminability of the objects in the test phase – but doesn’t this result simply reflect a lack of firing overall? The PV neurons sleep deprived animals appear to show very little activity during the test phase of the task (Figure 2I).\n\nThe authors claim that the proportion of object-following place cells among unstable cells is higher in the control group than in the sleep deprived group, but the difference appears miniscule (47.8% following in the Control group vs 52.5% in the Sleep-deprived group) and no statistical test results are provided for this comparison.\n\nThe authors provide a conclusion that is not necessarily warranted by the results of the co-active hippocampal cell analysis. Higher clustering coefficients were observed in the sleep deprived animals, but the statement that this clustering ‘impairs subsequent flexible object-location memory’ goes well beyond the data. The authors did not test the causal contribution of hippocampal cells to object location memory and therefore cannot make this claim.\n\nOverall, the methodology is sound and this paper presents an impressive series of experiments. It is a little unclear why sleep deprivation decreases medial septal PV cell activity, particularly when the animals were given a 12-15 hour recovery period following the deprivation, and one wonders if it isn’t the sleep deprivation per se that causes the change in behavior but rather the stress of unpredictable stimulation during the sleep phase that is responsible – but this is a caveat that can be explored in the Discussion section of the paper.", "reviewer_2": "The manuscript by Zheng and colleagues examines how medial septal (MS) PV+ neurons contribute to spatial memory and interact with hippocampal place cells. The authors combine sleep deprivation (SD), recordings from MS and hippocampus, and optogenetics to suggest that these neurons are important for object-place memory formation. While the recordings are technically well-done and the findings could be interesting, I have several major concerns that need to be addressed before possible publication.\n\nMajor Concerns:\n1. Sleep Deprivation Protocol and Potential Stress Confounds: The authors' SD protocol raises significant concerns about stress effects that could confound the interpretation of their results. While the authors attempt to control for stress effects by looking mainly at locomotion, several issues remain. The absence of expected sleep rebound following SD is particularly troubling, as this deviates from well-established sleep homeostasis principles (Borbély et al., 2016, Physiological Reviews). Moreover, the reduced exploration of displaced objects suggests potential stress-induced neophobia (Blanchard et al., 2001, Neurosci Biobehav Rev). Critically, if object exploration is indeed below chance level (this needs to be statistically tested), this would fundamentally challenge the authors' memory deficit interpretation, as it would suggest an anxiety-like phenotype rather than a memory impairment.\nTo address these issues, the authors should:\n• Measure stress hormones (corticosterone)\n• Compare their rotating bar method with gentler SD protocols\n• Include anxiety behavioral measures\n• Test object exploration against chance levels\n2. Neural Decoding and Cell Response Rates: The reported 97% decoding accuracy for object discrimination is particularly difficult to reconcile with the small proportion (16.3%, approximately 8 cells) of responsive MS PV+ neurons. Making this even more puzzling, different subsets of these few cells respond to different objects. Getting such high accuracy from so few cells seems unlikely, especially since even well-established sparse coding systems typically require larger active populations for reliable decoding (Huxter et al., 2008, Nature; Quian Quiroga et al., 2008, Nature Reviews Neuroscience).\nThe authors must provide:\n• Raw traces demonstrating how these few cells could carry such precise object information\n• Controls showing their decoder isn't simply capturing general behavioral states\n3. Place Cell Analysis and Object-Following Classification: The classification of \"object-following cells\" requires substantial revision. The current approach differs significantly from established methods for identifying object-related activity (Høydal et al., 2019, Nature). Place field shifts could reflect general remapping rather than specific object coding.\nThe authors should:\n• Examine whether place cells maintain consistent spatial relationships to objects across conditions\n• Control for random place field shifts\n• Compare their results with established object-vector cell properties\nMinor Concerns:\n1. Statistical Analysis:\n• Multiple comparison corrections are needed for cell classification\n• Sample sizes for correlation analyses are very small\n• Power analyses should be included\n• Effect sizes should be reported consistently\n2. Circuit Mechanisms: While the authors demonstrate effective direct pathway manipulation through PV+ terminal stimulation in CA1, they should also discuss potential indirect pathways, particularly the MS-entorhinal cortex-CA1 circuit, given the established role of the lateral entorhinal cortex in ‘tracking’ object location (Tsao et al., 2013, Current Biology).\nOverall, the study's key strength lies in its careful analysis of place cell co-activity patterns and network dynamics. A simpler and more conservative interpretation of the data would focus on the well-established role of MS PV+ neurons in theta generation and its impact on hippocampal function. The observed effects might be explained by:\n1. SD reducing MS PV+ activity, leading to decreased theta power\n2. Reduced theta disrupting place cell co-activity patterns\n3. Optogenetic activation restoring normal network dynamics\nThis interpretation better aligns with known principles of hippocampal function, particularly the established relationship between theta oscillations and place cell coordination. Theta rhythm has been shown to organize place cell ensembles (O'Keefe and Recce, 1993, Hippocampus) and modulate the temporal organization of cell assemblies (Buzsáki and Moser, 2013, Nature Neuroscience). Specifically, reduced theta power disrupts the temporal coordination of place cells (Dragoi and Buzsáki, 2006, Neuron) and affects the stability of hippocampal representations (Wang et al., 2015, Nature Neuroscience). Moreover, theta oscillations are known to coordinate the precise timing of place cell firing necessary for proper information encoding (Huxter et al., 2003, Nature).\nWhile the study presents technically impressive findings about MS PV+ neurons in spatial memory and includes valuable analyses of network dynamics, substantial additional analysis and significant tempering of claims are needed before the conclusions can be fully supported."}, "author_response": "Response:\nThank you very much for taking the time to thoroughly review our manuscript and for providing such valuable and constructive feedback. We sincerely appreciate your recognition of our technique, and we are especially grateful for your suggestions, which are truly helpful in guiding us to improve our work.\n\n[...]\n\nResponse:\nWe sincerely apologize for the oversight regarding the statistical details in our manuscript. Meanwhile, we realized that our analysis was primarily focused on the differences between the control and SD groups, rather than the interaction effects among different bands. Therefore, we use multiple unpaired t-tests to examine the differences between the two groups (control vs. SD) across various bands. We have revised this part accordingly in the figure legend (L804-805; L808-810).\nWe truly appreciate your comment, which prompted us to re-examine this aspect more thoroughly.\n\n[...]\n\nResponse:\nThank you for your question. Our original data show that, although the proportion of activated neurons in the SD group during the test phase appears to be higher than in the sample phase, the actual proportions (test control: 1:0:48; test SD: 3:1:45) did not show a significant difference based on the chi-square test (p=0.3505, c2=2.097, df=2). Meanwhile, to support the subsequent analysis, we have increased the number of animals used in the electrophysiological experiments and reanalyzed the data. Even after this addition, the proportion of neurons responsive to object exploration during the test phase still showed no significant difference (Legend Fig 3J).\n\n[...]\n\nResponse:\nThank you for your valuable comment. After supplementing the dataset with additional neurons, we also refined our analysis methods. Within-group comparisons were conducted using one-way ANOVA with Bonferroni’s post hoc test. We have updated the corresponding description in the Results (L125-131) and legend (L818-822) section accordingly.\n\n[...]\n\nResponse:\nThank you for your comment.\nFirst, we have provided the actual firing rates across the four phases in the supplementary material (Fig. S3C), and no statistically significant differences were observed between control and SD groups. These results have also been explained in the revised Results section (L125-130). Second, the neuronal population input into the decoder was based on normalized data (Z-score, L657-660), so the actual firing rates would not influence the decoding performance. Additionally, a study by Kao et al. (2013)1 demonstrated that applying firing rate normalization in brain-machine interfaces enhances robustness against perturbations in firing rates, such as those caused by recording loss or external noise. The normalized firing rates can lead to more stable and reliable decoding performance, independent of the actual firing rates2,3. Therefore, employing normalization techniques like Z-scoring is a standard practice in neural decoding to ensure that the decoding performance is not adversely affected by the absolute firing rates of individual neurons.\nMeanwhile, we have improved our decoder by generating a shuffled distribution for each group’s Z-scored firing rates to account for chance-level decoding (Fig 4E-H).\n\n[...]\n\nResponse:\nI sincerely apologize for this mistake, and we are truly grateful to the reviewer for carefully identifying it. The labels for “stable” and “unstable” in the figure were indeed incorrect. We have now corrected this error and thoroughly checked the manuscript for consistency. As shown in Fig. 5A, “unstable” clearly refers to place cells that underwent remapping, and this is also described in the Results (L198-200) and Discussion(L400-401) section, where we state that the majority of place cells exhibited remapping (unstable cell). We have provided a more detailed explanation of the definitions of “unstable cell” and “stable cell” (L196-198). It is worth noting that, for the sake of accuracy and to better align with the additional analyses we included later, the term “following” in the figure has been revised to “near.”\nWe deeply apologize once again for the confusion it may have caused.\n\n[...]\n\nResponse:\nThank you for your insightful comment. Indeed, considering the two types of behavior described in the literature4, it is not appropriate to use the term “flexibility” in a comparative sense for our behavioral paradigm. We have therefore revised this claim. Our results indicate that after SD, place cells exhibit altered population firing patterns during the encoding phase. We have updated this description accordingly in both the Results (L224-226, L231-233) and Discussion (L415-420) sections. We also supplemented the analysis with the place cell co-activity observed during the un-moved OPR test (Results: L233-243; Discussion: L420-423)\nWe appreciate the reviewer’s suggestion, which helped us better clarify our interpretation.\n\n[...]\n\nResponse:\nThank you for your valuable comments and positive feedback on our manuscript. We have added the relevant discussion section (L440-448).\n\n[...]\n\nResponse:\nThank you very much for your valuable suggestions and comments. When designing our behavioral paradigm, we did take anxiety into account, particularly considering how anxiety might affect the exploration time of objects in mice. Therefore, we included total exploration time as an index to validate the appropriateness of our paradigm (Figure 1E, Fig S1C).\nAs for the rebound sleep phenomenon you mentioned, it is generally observed after prolonged sleep deprivation (e.g., 24 hours)1. In contrast, our study employed a milder and intermittent form of SD, using a low-speed rotating bar that operated for 1-2 minutes every 15-20 minutes. We have also expanded the Discussion to address these points in greater detail (L329-333).\nSpecifically, we added a gentle-handling SD group and compared plasma corticosterone levels and elevated plus maze (EPM) behavior across three groups. The results showed no significant differences among the groups. These additions have been incorporated into the Methods(L481-483), Results(L99-103), and Discussion (L323-326) sections.\n\n[...]\n\nResponse:\nWe greatly appreciate and carefully consider each of your suggestions. We have included the raw traces of MS PV+ neuron activity in the revised manuscript to provide a more intuitive representation of the recorded signals (Fig. 3E).\nWe agree that the sparse expression of the optogenetic virus and the relatively deep location of the MS indeed posed technical challenges for our recordings. To address this, we added recordings from five additional mice implanted with optrodes in order to maximize the number of tagged MS PV+ neurons captured (legend Fig.3, 4). The data from these additional mice have been fully integrated into the overall analyses and results throughout the manuscript.\nRegarding the decoder construction, we recognize that using the ‘fitclinear’ method might introduce issues such as linear overfitting and limitations in handling the high-dimensional nature of neural data. Therefore, we replaced it with a GLM decoder, as supported by previous literature, which is more appropriate for modeling the relationship between neural activity and specific behaviors2. Additionally, due to transfection efficiency of optogenetic viral, some untagged neurons, not identified by light stimulation, might still be PV+ neurons. However, we used this untagged group as a control to emphasize the behavioral specificity of the tagged PV+ neurons, acknowledging that the non-tagged group may be more susceptible to influences from other neuron types during encoding (Fig 4H).\nTo ensure that the decoding performance reflected true specificity to object exploration behavior rather than chance level, we shuffled each dataset of different groups 1000 times to establish a chance-level baseline (Fig.4E-H; Method: L656-670; Result: L168-L185; Discussion: L381-398).\n\n[...]\n\nResponse:\nThank you very much for your insightful suggestions regarding our analytical approach. We acknowledge that there may have been some issues in how we originally framed our analysis throughout the manuscript. While grid cells in the medial entorhinal cortex (MEC) are known to include object-vector (OV) cells that encode vectorial relationships to discrete objects in the environment3, the place cells in the dCA1 are more likely to undergo re-encoding the spatial changes. Therefore, we believe it is more appropriate to analyze the overall dynamic changes in place cell activity in response to object displacement.\nBased on this understanding, we recognize that our previous analysis approach had certain limitations. We have revised our analysis to include all place cells and examined the overall shift patterns in spatial firing following object displacement (Fig 5D-F). We have also added further results and discussion to reflect this refined approach and its implications (Method: L690-705; Result: L200-223; Discussion: L408-423).\n\n[...]\n\nResponse:\nThank you for your comments regarding the statistical analysis.\nWe fully recognize the importance of properly addressing multiple comparisons in categorical data. While the chi-square test is useful for detecting overall differences across multiple groups, it does not inherently specify which particular group pairs differ. Although post-hoc pairwise comparisons (e.g., pairwise chi-square tests with Bonferroni or FDR correction) can be applied, there is currently no universally standardized method for multiple comparisons following a chi-square test, particularly when cell sizes are uneven. In our case, we were primarily interested in the pairwise differences in proportions between specific groups, particularly comparisons involving the control group or the SD group, rather than testing for global differences across all groups. Therefore, we conducted pairwise Chi-square tests between groups and highlighted the group comparisons that showed significant differences. Nonetheless, we remain open to further revising our statistical approach should you have any specific suggestions.\nWe have supplemented the correlation analyses with data from mice in which MS PV+ neurons and dCA1 place cells were recorded simultaneously. This resulted in an increased number of recorded neurons, thereby enhancing the robustness and reliability of our findings (see Legend Fig. 6).\nWe have provided a more detailed description of the statistical methods used for the LFP analysis (see Legend Fig. 2). We also examined the changes in LFP theta oscillations during the exploration and non-exploration periods (Fig S2D).\nWe sincerely apologize for this. We have added a detailed explanation in the figure legend. If there are any remaining concerns, we are more than willing to provide further clarification or make additional revisions.\n\n[...]\n\nResponse:\nThank you for these comments.\nGiven that the projections from MS PV+ neurons to the EC are known to play a significant role in navigation4, we have added a corresponding discussion in the revised manuscript(L449-455).\n\n[...]\n\nResponse:\nWe sincerely appreciate your careful review of our manuscript and your valuable assistance in helping us clarify our logic. We fully acknowledge your comments and have reflected them in the revised analyses. Although we have examined the co-firing of PV+ neurons and place cells, due to the limitations in electrode placement and the location of the recordings, we also believe that the modulation of theta rhythm played a key role in altering the firing patterns of place cells. We are more focused on the dynamic changes within the place cell population. We have emphasized this point in both the abstract (L29-35) and the discussion (L438-446) sections of the revised manuscript."}, {"round": 2, "reviewer_comments": {"reviewer_2": "I want to congratulate the authors on their substantial efforts to address my previous concerns. The addition of anxiety controls, expanded statistical analyses, and methodological clarifications represent significant improvements to the manuscript. The core findings about MS PV+ neurons' role in spatial memory are compelling and well-supported. The technical quality is high, and the authors have been responsive to this reviewer feedback.\n\nWhile the study makes important contributions to understanding MS-hippocampal circuits in spatial memory, there are several methodological refinements that would strengthen the conclusions and ensure the most robust interpretation of the data.\n\n1. The object-place recognition behavioral results would benefit from statistical comparison against chance level (50% preference). This would definitively establish memory impairment versus active novelty avoidance and provide a stronger foundation for the cognitive interpretation. A simple one-sample t-test against 50% would suffice for each group and represents a standard control in recognition memory studies.\n\n2. The speed-firing rate correlation analysis requires methodological revision. The current approach of correlating 1-second binned firing rates with velocity creates a mismatch between temporal and behavioral scales. I suggest binning data by speed ranges (e.g., 0-5, 5-10 cm/s, etc.) and calculating mean firing rate per speed bin, then testing correlation across speed bins rather than across time bins. Additionally, given the apparent non-linear relationships visible in Figure 4, non-parametric correlation (Spearman) might better capture the true speed-firing relationship.\n\n3. The GLM decoder analysis uses a 3-second post-exploration window that is quite long relative to typical object exploration episodes, which often last less than 1 second. This same temporal window concern applies to the neuronal response classifications in Figure 3. While this doesn't invalidate the core findings about MS PV+ involvement, it would be valuable to acknowledge in the discussion that this temporal window may capture post-exploration activity and could influence decoding accuracy estimates. The precision of object discrimination claims should therefore be interpreted with appropriate caution.\n\n4. Some of the circular distributions in Figure 7 appear bimodal, which may not be optimally suited for Rayleigh testing, as this test assumes unimodal distributions. While this doesn't compromise the overall findings, acknowledging this statistical limitation or exploring alternative circular statistics approaches would enhance methodological rigor.\n\n5. Several figures would benefit from improved clarity through the addition of panel subtitles or clearer labeling. For example, the multiple components in Figures 3, 4, and 5 could be made more accessible to readers with descriptive subtitles that guide interpretation of the complex multi-panel displays.\n\nThis study clearly demonstrates that MS PV+ neurons influence spatial memory through theta regulation and place cell dynamics, representing a significant advance in understanding septo-hippocampal circuits. With these refinements, this will be a strong and impactful contribution to the literature.", "reviewer_3": "Zheng and colleagues explored how medial septal parvalbumin immunoreactive (MS-PV+) neurons regulate hippocampal object location coding and memory in control and mildly sleep deprived (SD) mice. They demonstrated the modulation of MS-PV+ activity by object exploration and correlated activity of these neurons with certain hippocampal place cells. Sleep deprivation was shown to impair object location memory paralleled by decrease of theta (both power and number of bouts), drop in the number of object-modulated MS-PV+ neurons, suppression of MS-dorsal CA1 correlation and vanishing of place cells’ remapping bias toward new object location. Propensity of place cell coactivity was also found to be higher in SD animals. Optogenetic stimulation of MS-PV+ neurons could partially rescue the SD-induced object location memory impairment. A key strength of the study is the systematic analysis of correlated alterations in the hippocampal and medial septal circuits. I would highlight three novel findings: i) the increased tendency of place cell cofiring after SD as a possible cause of memory impairment; ii) differential, object-associated activity of MS-PV+ neurons; contribution of MS-PV+ to spatial memory updating beyond theta genesis by facilitating remapping of place cells. Notably, the manuscript I received is an extensively revised version of an original in which many criticisms of the other reviewers had already been resolved. However, I have found many issues that need to be addressed before publication. My concerns are the following:\n\nFirst, a minor but important remark about terminology: MS-PV+ neurons (generally, MS neurons) are not interneurons (neurons with axon arbors restricted to and not projecting out of a given brain region) but projection neurons. The term “interneuron” is a bit misused even in hippocampal terminology because of historical reasons (i.e. several long-range projecting hippocampal GABAergic cell type are also called interneuron, because initially, some of these subtypes were thought to be classic interneurons). Thus, use MS neurons instead of interneurons.\n\nBased on the presented data, I am unsure about the identity of the recorded MS neurons. Since it is a central part of the study, more information should be given about these neurons. First, show (as supplementary) the reconstructed electrode tracks / locations both in the MS and in the hippocampus. It is particularly important in the former case because mistargeting (especially in the ventral direction) could have resulted in sampling PV+ neurons from below the MS / vertical limb of the diagonal band of Broca (vDB) and these ventral populations have different targets and functions than the septo-hippocampal PV+ neurons. Second, the example of optotagging on Figure 3 is not convincing. Based on the scale bar, the latency from the end of the laser pulse to the spike burst is about 20-25 ms on panel A, which is too long for optotagginng. On the raster (panel B) and PSTH (C), the latency is in the right range (< 10 ms), but on the raster, number of spikes is low, and jitter of spikes looks too large. Third, the low rate of the neuron on Figure 6A (voltage and time scale bars are missing) is in disagreement with what is known about the firing rate and pattern of MS-PV+ neurons. Fourth, based on the velocity-dependent firing rate histograms on Figure 4B, firing rate of MS-PV+ was unexpectedly low. According to previous studies, these neurons fire much faster (see for example Joshi et al., 2017 or Borhegyi et al., 2004). Together, these characteristics raise the possibility that a mixed population incorporating both MS and extra-MS PV+ populations were recorded. A convincing evidence refuting this possibility would be to show the recording sites (tracks, as mentioned above) and, as a further support, a table with state-dependent firing rates and a metric of theta modulated activity (like theta modulation index along with a few example autocorrelograms).\n\nAlong this line, lack of speed-correlation is also surprising and goes against preceding reports (King et al., 1998) therefore, this discrepancy should be discussed.\n\nThe differentiation of objects by MS-PV+ activity is quite striking. Please, discuss which aspect of their activity would be responsible for this phenomenon. Additionally, two supporting analyses may strengthen and cross-validate this finding: calculation of object decoding accuracy for every MS neuron (PV+ and non-PV) and presenting the distribution of accuracies. Additionally, comparison of basic firing characteristics (firing rate and rhythmicity) near the two objects. The latter would reveal which aspect of activity supports object classification.\n\nFor the analysis of interaction between MS-PV+ neurons and pyramidal cells, cross-correlation would provide higher temporal resolution and would complement the correlation based on binned firing rate time series. Also, on Figure 6A, the presented correlated pair is not convincing (and, as pointed out above, the activity of the putative MS-PV+ neuron does not look like the activity of a typical MS-PV+ neuron). Please, show a better example.\n\nIt was stated that “Notably, the place cells correlated with different MS PV+ neurons are not the same neuronal ensemble (Fig. 6C).” but it was not tested explicitly. This claim would be supported by presenting the average overlap (%) between place cells correlated with any given MS-PV+ neuron. If this proportion is low, the Authors’ claim is verified.\n\nPlease show example time-frequency plots for supporting the claims about the effect of 12 Hz optogenetic MS stimulation on hippocampal LFP.\n\nMS-PV+ neurons were claimed to lack place fields. However, the examples (especially the leftmost) on Figure S3G prove the opposite: these neurons exhibit quite robust spatially modulated activity. It is also worth mentioning that peak firing rates on these maps are lower than would be expected from previous recordings of MS-PV+ neurons.\n\nThe sentence “The electrical stimulation of the medial vestibular nucleus in anesthetized rats modulated the firing of single hippocampal neurons, including complex spiking neurons identified as putative \"place cells\".” in the Discussion is a bit out of context.\n\nA few sentences later, the Authors suggest that their failure of detecting monosynaptic connections between MS-PV+ neurons and pyramidal cells is due to technical reasons (“…due to limitations in electrode recording sites and the number of recorded neurons…”, lines 439-440). This sentence has to be deleted, because according to a long line of functional anatomical reports, there are no monosynaptic connections between MS-PV+ neurons and hippocampal pyramidal cells. The former is interneuron-selective, and disinhibition via the rhythmic suppression of hippocampal interneurons is the key mechanism whereby pyramidal cells’ activity is affected by the MS GABAergic pacemaker circuit (Freund and Antal, 1988; Toth et al., 1997).\n\nFurther down in the Discussion (lines 443-444), it is said: “…we tend to attribute the observed effects to the regulation of dCA1 theta rhythms by MS PV+ neurons, which in turn modulates the firing patterns and structural properties of place cells.” What kind of structural properties do the Authors refer to (I am not aware of any study showing structural /morphological(?) changes of pyramidal cells following MS stimulation)?\n\nA few lines later (447-447) the following was written: “The reciprocal projections between PV+ neurons in the MS and dCA1 may also be a potential cause of abnormal MS PV+ neuron firing rates observed during the formation of the spatial task.” This sentence should be corrected because the majority of hippocampo-septal feedback neurons are not PV-expressing but somatostatin (Sst)-positive.\n\nIn the Methods, the Authors state that: “To measure the responses of place cells to PV+ stimulation in the MS, mice in SD groups received optogenetic stimulation either during running…” Does it mean that stimulation was delivered in a closed-loop fashion as a function of movement? If yes, how running-gated close-loop stimulation was implemented?\n\nPlease clarify, how shuffling was done (interspike intervals were shuffled, or analysis windows were mixed, or spiketimes were shifted randomly…)?\n\nThreshold spatial information for qualifying a pyramidal cell as a place cell was claimed to be 0.3 s/spike. Isn’t it 0.3 bits/spike?\n\nAs stated: “The remapping value was estimated by averaging the Pearson R correlation values across cells in each phase.” Please clarify, what was correlated (spatial maps…)?\n\nIn the GLM, a weighing parameter was used (alpha-i,j). How was it determined?\n\nThe manuscript, especially the Methods section should be checked and typos, grammatical errors / inconsistencies have to be corrected. For example, there is no predicate in the sentence “We next asked how the firing activity of place cell populations in object-location memory.” Or this sentence is barely intelligible: “Three ensemble neurons showed no differences in all recorded MS interneurons between the control and SD groups (Fig. 3F and 3G).” Or: “This might result from SD alters the firing patterns of MS PV+ neurons, diminishing their ability to recognize objects placed in distinct positions.” Wording should be changed because neurons are not able to “recognize” only their activity is tuned to or modulated by something (e.g. objects). Many other examples can be listed.", "reviewer_4": "Zheng et al investigate the involvement of Medial Septum (MS) GABA-releasing neurons in the deficits observed in an object-place recognition (OPR) test using a rodent model of sleep-deprivation. They study the role of MS-hippocampal communication by performing recordings of opto-tagged MS parvalbumin-expressing inhibitory neurons and dorsal hippocampus neurons. The main findings are: i) an SD protocol that causes deficits in OPR, ii) altered hippocampal oscillations and MS-HPC coherence in SD mice during the sample test of the OPR, iii) altered MS PV+ neuron firing in OPR task in SD mice, iv) altered remapping in dorsal CA1 place cells in SD mice, v) activation of MS PV +INs restores oscillations & coherence in a OPR-phase specific manner and remapping of CA1 place cells, vi) manipulations of MS PV +INs (and their projections to CA1) during the sample phase have consistent effects on OPR test.\n\nThe hypothesis of the study is original, coherently based on previous knowledge about MS-hippocampal communication and of general interest for neuroscience, in particular for the mechanisms underlying spatial memory formation and its perturbation by sleep deprivation. The manuscript has strengths (such as the results showing place cell activity remapping and the results of the optogenetic manipulations of MS-HPC connections in the context of OPR task performance). Overall, I believe that the manuscript has the potential of making a considerable impact in the field. I write “potential” because I believe that substantial work is necessary before publication since some critical issues arise during reading. This points needs to be fully should be resolved to provide solid support to the conclusions of the manuscript. In particular, the issues refer to: I) Anxiety and hippocampal effects of the SD protocol, ii) statistical analysis, iii) analysis to assess locomotion influence on MS neuronal activity, iv) consistency of decoder results with the experimental findings.\n\nI develop these points (and others) in major a minor comments below.\n\nMAJOR\n1) The authors use two different protocols for SD. Surprisingly, the experiments aimed to assess anxiety in SD mice are only performed in one of them (the gentle protocol, Fig. S1F-H) and not in the rotating bar protocol. Thus, the statement in lines 102-103 only applies to the “gentle” protocol and anxiety confounds in the other protocol (rotating bar) are not fully resolved. Since the rotating bar protocol is used in the experiments that support the main conclusions of the manuscript, this poses a serious problem. Authors must assess the potential involvement of anxiogenic effects of SD using the same protocol (rotating-bar) used in the OPR and electrophysiological recording experiments.\n2) Fig. 2E. SD is often reported to alter SWRs. Since SD used by authors do not resulted in SWR rate changes, it is of interest to analyse whether SD affects other aspects of SWRs such as internal frequency or power. Overall, the effects of SD seem mild. To strengthen the interpretability of the results, it is important to understand which effects of the SD protocol used in the current manuscript can be aligned with previously reported effects and which can not.\n3) Data in Fig 3C should be analyzed with a 2 way ANOVA and the interaction term (Behavioral session x sleep) reported. Similarly, in Fig 2D, but for exploration x sleep factors. In both cases, the effects of SD on PV firing rates should be checked. Is the firing rate of MS PV INs affected by SD? Is this effect contingent on the behavioral session or the type of neurons (responsive or non responsive)? What is the rationale for analyzing firing rates in some instances and their correspondent Z-score in others?\n4) The statement “The overall proportion of neurons responding to the objects was relatively low in the SD group (Fig. 3I and 3K) (lines 151-152)” must be supported by statistical analysis. The same for lines 156-157 (Fig. S3G).\n5) I believe that the selected method to analyze the potential influence of locomotor velocity on MS PV+ neuron may not be optimal to detect it. First, the analysis should be run separately in C and SD mice to detect differences induced by this manipulation. Second, instead of analyzing the speed-activity in relationship in all recorded neurons, I think it would be more appropriate to first detected speed modulated neurons (PV or untagged) and then relate this with the different categories used to analyze their relationship with the OPR test (Non-responsive, Excited, inhibited). This analysis will complement the current one and would strengthen the conclusion about the absence of locomotion modulation of OPR relevant neurons.\n6) The decoder results in Fig. 4 suggest altered PV+ neuronal activity during the test phase in SD group, since the recorder failed in this specific condition. How do the authors reconcile the results of the decoder with a main conclusion of the paper, i.e. that activity of MS PV+ IN during encoding (sample) is critical for performance on the OPR task?\n7) Regarding CA1 place cells in C and SD during the test phase, it is important to analyze whether a bias toward object location was detected in place cells and whether this bias is affected by SD? Are object locations equally represented by place cells in C and SD groups during the test phase? Are the place field of unstable place cells at the same distance from the objects during the sample phase in both groups?\n8) A general comment on the reporting of the statistical data. Only the p values is provided when reporting statistical test results in multiple instances in the manuscript. In some instances, no details are provided at all. Authors must provide details of the statistical analysis used to support all their claims, in the main and supplementary figures (n, t, F …).\nMinor.\n1) Time course of sleep analysis after sleep deprivation (Fig. S1A, B) does not reflect a recovery of wake or NREM sleep and shows a continued decrease in REM sleep. Authors should discuss how this results are reconciled with the changes observed during sleep deprivation and provide an explanation of why a sleep rebound (increased time or bouts) is not observed in SD mice with respect to control.\n2) Data plotted in Fig. 1E must be tested against chance levels (50%) using appropriate statistical tests. Same for data in Fig. 5D, but for distance change = 0.\n3) Since no statistical details are reported it is unclear whether a difference between exists between Control and SD in data plotted in Fig. S2D. The separate and interactive effects of exploration and SD must be properly analyzed and reported (for example, using a Two way ANOVA).\n4) State clearly thought the manuscript which SD protocol is been evaluated, clearly identifying instances were gentle protocol or the rotating bar protocol is used.\n5) Fig. 2E. Only the start of the exploration epoch is clearly depicted. Arguably, exploration epochs are of different duration. Information should be provided about the duration of the epochs represented in Fig. 2E and on the criteria used to time stamp the start and the end of an exploration epoch.\n6) X axis labels are missing in Fig. 4F-H.\n7) Sentence in lines 258-259 correspond to a comparison (C vs SD) that is not showed in Fig. 6. Authors show that the increase in the number of correlated pairs observed in the C group could not be observed in SD group.\n8) Why is remapping observed only in the x-axis?\n9) Typo. Missing verb in sentence lines 224-225. Line 365 “recoding”. Do author mean encoding?\n10) The meaning of the sentence “Notably, the place cells correlated with different MS PV+ neurons are not the same neuronal ensemble” (lines 256-257) should be clarified.\n11) Fig. 5H represents adjacency matrices for sample or test sessions? No scale is present for color code (white, red, blue).\n12) In line 280-282, authors make reference to data in Fig 7D and SF5F that are difficult to reconcile. Fig. 7D shows higher proportions of place cells moving close to the displaced object in mice with MS PV+ INs stimulated during¡g the sample phase compared to rest or test. However, as far as I understand the labels in SF5F, this proportion is not different between the laser groups (LS, LR and LT) and is lower compared with the SD (no laser group). Could authors clarify this apparent discrepancy?"}, "author_response": "Thank you for your positive comments and for recognizing our revision. We also sincerely appreciate your review, which has helped us improve the quality of our manuscript.\n\n[...]\n\nThank you very much for your comment regarding the behavioral analysis. We have now added a one-sample t-test analysis against the chance level (50%) for the OPR behavioral data (Fig. 1, Fig. 8 and Fig. S10).\n\n[...]\n\nWe thank you for your valuable suggestion regarding the speed-firing rate correlation analysis. We acknowledge that our initial analysis was relatively coarse and broad. Based on your advice, we have added more detailed analyses. Specifically, we used Spearman correlation to assess the relationship between firing rate and locomotor speed for each neuron. Neurons were then grouped into positively correlated and negatively/non-correlated categories, and we examined the overlap of these groups with neurons responsive to object exploration. This additional analysis complements our previous findings, further supporting the conclusion that locomotor activity has a limited influence on OPR-relevant neurons.\n\n[...]\n\nThanks for your question. We defined exploration as being within 3 cm of the object, based on the fact that the body length of an adult mouse is approximately 8 cm. and our video tracking software primarily used the nape of the mouse as the reference point for trajectory detection (Fig R1). This threshold thus provides a reliable measure of head proximity to the object. To avoid false exploration events, we excluded trials with exploration durations shorter than 1 s.\n\n[...]\n\nThank you for pointing this out. We agree that several circular distributions in Figure 7 exhibit bimodality, which violates the unimodality assumption of the Rayleigh test. Since Rayleigh is most sensitive to unimodal von Mises-like alternatives, it may underestimate non-uniformity in multimodal data.\nTo address this, we reanalyzed the data with Watson’s U² and Kuiper’s tests, which are omnibus tests for circular uniformity and do not assume unimodality. Both yielded results consistent with our original conclusions (e.g., control group: Kuiper: V = 0.0928, p = 0.0090; Watson’s U²: U² = 0.2785, p = 0.0096), strengthening the robustness of our findings.\n\n[...]\n\nThank you for this helpful suggestion. You are absolutely right that adding clearer panel subtitles and labels would improve readability. We have revised Figures 3-5 and other figures by adding descriptive subtitles, aiming to better guide readers through the interpretation of the results.\n\n[...]\n\nWe sincerely thank you for your careful and thoughtful review of our manuscript. We greatly appreciate your recognition of the novel findings in our study, as well as the valuable comments and suggestions you provided. Your feedback has been in further improving the quality of our work. We have carefully considered all the issues you raised and have addressed them thoroughly in our revised manuscript.\n\n[...]\n\nThank you for pointing out this important clarification regarding MS neurons. We have carefully revised the manuscript to consistently refer to these cells as “MS neurons” instead of “interneurons”.\n\n[...]\n\nThank you for your careful evaluation and for recognizing the originality and significance of our study. We greatly appreciate your encouragement. We are also very grateful for the reviewer’s rigorous and constructive comments, which will undoubtedly help us to further improve the quality of our work. We have carefully addressed the concerns raised."}, {"round": 3, "reviewer_comments": {"reviewer_2": "The authors have addressed all major points raised in the previous round, and the new analyses strengthen the manuscript. However, the revision has introduced several patch-like additions throughout the text. In its current form, the paper reads somewhat fragmented: methodological explanations and justifications appear directly inside the Results, interrupting the narrative flow. I would strongly recommend a final editorial pass to harmonize the writing, streamline the Results, and move technical clarifications to the Methods. This would make the manuscript much more cohesive and easier to follow, without requiring any new data.", "reviewer_3": "The Authors successfully addressed my concerns. Only a few errors remained in the revised manuscript.\n1. One is this sentence from the Discussion:\n“MS PV+ neurons do not project directly to dCA1 pyramidal cells but can influence them indirectly through disinhibition of hippocampal PV+ neurons.” Disinhibition acts by suppressing the activity of inhibitory interneurons (not only PV+ ones but practically all subtypes) and as such releasing pyramidal cells from inhibition. It’s not the PV+ neurons that are disinhibited.\n2. Object-excited and -inhibited MS neurons are called excitatory and inhibitory, respectively. However, it’s misleading, especially in case of PV+ neurons because these are categories based on the effect the given neuron exerts on its targets. Therefore, please change excitatory / inhibitory to excited / inhibited instead.\n3. On Figure 4 panel E MS neurons are still called interneurons. Please change it to neurons.", "reviewer_4": "I thank the authors for taking into considering my comments. I appreciate the efforts aimed to clarify the misunderstandings and resolve my comments on the original version of the manuscript. With a minor exception, in the new version of the manuscript, the authors have satisfactorily addressed all my comments.\nI have only a minor issue regarding my comment #3, in the part related with Fig 3D. Here, I do not fully understand authors’ response. They mention that the goal was to verify light stimulations parameters under baseline conditions (?), but my concern was about the statistical analysis of the object exploration (Ex/Non-ex) effects on MS PV+ neurons. In any case, authors perform the analysis of this data using raw firing rates, which I believe is correct. Authors use, however, multiple t-test to analyze data in this figure, which, without proper corrections, increases the risk of false positives. If they choose to take this risk, they should avoid to extract conclusions from comparison of from C and SD groups (lines 143-144), since those were not included in the statistical analysis performed."}, "author_response": "We sincerely thank you for your positive evaluation of our revision and for the valuable suggestions you provided. We also greatly appreciate your patience and professional guidance during the review process, which helped us further enhance the quality of our study. We fully agree with your comments regarding the writing and overall presentation of the manuscript. In response, we revised the manuscript to harmonize the writing style, remove redundant sentences, streamline long passages in the Results and Discussion sections, and reorganize paragraphs for improved readability. We hope these revisions have substantially enhanced the coherence and readability of the manuscript.\n\n[...]\n\nWe apologize for the misunderstanding regarding the referenced mechanism and greatly appreciate your expertise and careful attention. We have revised the sentence in the Discussion to more accurately reflect the role of MS PV+ neurons.\n\n[...]\n\nWe sincerely thank you for your valuable suggestions, which have greatly helped us improve the quality of our manuscript. We are also glad that our revision has addressed your concerns.\n\n[...]\n\nThank you very much for your rigorous comments and suggestions regarding the statistical analyses. First, the light-stimulation parameters were selected based on the results of electrophysiological data (LFP and firing rates). As clarified in L301-303, “Based on the average firing rate of MS PV+ neurons during the OPR test, we selected a 12 Hz frequency, within the theta band and above 10 Hz, to activate MS PV+ neurons.” Second, regarding the concern about false positives, our analysis in Fig. 3D indeed need to focused on within-group comparisons of firing rates during exploration versus non-exploration periods for the same neurons. Therefore, paired t-tests were appropriate for assessing the Ex/Non-ex effect within each group, and each group contained only one planned contrast, this analysis did not involve multiple independent hypothesis tests that would require correction across groups. The previous legend note “*p < 0.05, **p < 0.01, and **p < 0.001 compared to control-ex” was ambiguous, and we have revised it accordingly. The description in original L143-144 (now L133-135) could be misleading, as it is not directly supported by the statistical analysis. We have revised this sentence to more accurately reflect the results. We sincerely apologize for the previous ambiguity and lack of rigor in our statistical descriptions, and we greatly appreciate your constructive feedback, which has helped improve the clarity and rigor of our manuscript. We believe that these revisions substantially enhance the clarity and statistical transparency of the work."}]

Decision letter
[Editorial Note: See attachment]