Anterior and posterior retrosplenial cortex form distinct visuospatial circuits in the mouse

Domain: biology
Published: 2026-03-25
Editorial outcome: major_revision

Abstract
The retrosplenial cortex (RSC) integrates sensory and mnemonic information to support spatial orientation and navigation, yet how visuospatial processing differs across its subregions remains unclear. Here, we combined cellular imaging in navigating mice with brain-wide tracing of anatomical inputs to characterize how multimodal sensory and positional signals are integrated along the anterior–posterior axis of dorsal RSC. We identified systematic differences between anterior and posterior subregions in both functional response properties and long-range connectivity. Anterior RSC neurons displayed sharper and more reliable position tuning during tactile-cued navigation and preferential sensitivity to fast, low–spatial-frequency visual motion. In contrast, posterior RSC neurons showed broader position selectivity, stronger responses to slow, high–spatial-frequency visual patterns, and enhanced tuning in visually immersive virtual environments. Consistent with these differences, anterior RSC received denser projections from motor, somatosensory, and parietal areas, whereas posterior RSC was more strongly innervated by primary and posteromedial visual cortices. Together, these findings delineate an anterior–posterior functional gradient in RSC in which subregions integrate distinct combinations of sensory inputs to support navigation.

Introduction
The retrosplenial cortex (RSC) is a central hub for integrating sensory, mnemonic, and contextual information to support navigation, learning, and memory1–5. Through its extensive connections with sensory, motor, thalamic, and hippocampal circuits6–9, the rodent RSC is well positioned to transform multimodal information into behaviorally relevant representations that guide navigation and decision-making10–13. Despite this central role, the organizational principles governing these multimodal information flows within the RSC remain poorly understood.

During spatial navigation, the brain must continuously update the animal’s position and orientation by integrating self-motion cues with external sensory landmarks14. RSC neurons encode diverse spatial variables, including landmarks11,15, reward locations16, head directions17–19, and egocentric boundaries20–22, highlighting its potential role in aligning internal representations with external space. These representations are essential for spatial learning and memory, and RSC lesions or inactivation produce profound navigational impairment15,23,24. Vision plays a particularly important stabilizing role: through reciprocal connections with visual and hippocampal systems, the RSC links sensory cues with long-term spatial memory. RSC neurons exhibit both visually evoked activity and position-selective responses25–28, suggesting that this region serves as an interface between sensory perception and internal spatial maps. Together, these findings demonstrate the diversity of spatial signals present in RSC but do not explain how these signals are organized across its subregions.

Anatomically, RSC spans the anterior–posterior extent of the dorsal posterior cortex and contains dorsal (RSCd) and ventral (RSCv) subdivisions, which differ in lamination architecture and connectivity9,29,30 and support distinct memory-related behaviors31,32. While these dorsal–ventral subdivisions have been well characterized, functional and anatomical distinctions along the anterior–posterior axis are less well understood. Posterior RSC has been implicated in contextual and fear-related memory encoding33 and receives denser inputs from visual cortices, supporting its role in scene and context processing28,34. In contrast, anterior RSC shows stronger coupling with motor areas, consistent with its involvement in action planning and motor integration7,34. How these anterior–posterior differences shape visuospatial processing during navigation—and how they relate to long-range connectivity—remains largely unresolved.

Here, we combined two-photon calcium imaging during navigation with brain-wide retrograde tracing to characterize how visual and position-related signals are integrated along the anterior–posterior axis of the mouse RSC. We show that anterior and posterior RSC subregions exhibit distinct specializations: anterior RSC neurons show more robust and reliable position tuning and are preferentially driven by fast, low–spatial-frequency visual motion, whereas posterior RSC neurons are tuned to slow, high–spatial-frequency stimuli and display stronger position coding when visual landmarks are prominent. These functional distinctions were mirrored by differences in long-range wiring, with sensorimotor and parietal projections enriched in anterior RSC and visual cortical projections enriched in posterior RSC. Together, our results reveal an organization in which distinct RSC subregions integrate different combinations of sensory cues to support navigation.

Results
We combined functional cellular imaging with long-range anatomical input tracing to study the regional specialization of neuronal response properties in the mouse retrosplenial cortex (RSC) as well as that of afferent inputs (Fig. 1A). We focused on anterior and posterior RSC subregions, which differ in lamination architecture and connectivity9,29,30, support distinct behaviors31,32, and thus may show distinct neuronal responses and input patterns. We first characterized responses across anterior–posterior RSC subregions and then investigated brain-wide inputs to these subregions. To characterize functional properties, we initially investigated how visual and position-related signals are encoded across neuronal populations in RSCd subregions (Fig. 1B).

GCaMP6s-expressing mice were implanted with a chronic cranial window and trained to run on a linear treadmill. While the animals ran, a water reward was delivered at a fixed location, and tactile landmarks were affixed to the treadmill belt, providing salient cues about position along the track. This cued-belt paradigm reliably elicits position-related activity in hippocampal and retrosplenial areas27,35,36. The cellular imaging targeted layer 2/3 neurons in dorsal RSC (RSCd). Sampling of deeper layers and ventral RSC was limited. Using two mouse lines, we recorded from large populations of cortical excitatory neurons (Thy1-GCaMP6s, n = 7 animals; and CaMKII-tTA × TRE-GCaMP6s, n = 8 animals; Supplementary Tables 1 and 2, see Methods). Each recording began with a 15-minute period during which the visual display showed a uniform gray screen. This was followed by a 30-minute block of full-field visual stimulation.

The main data set characterized RSC activity while animals ran on a treadmill with tactile landmarks. For subsequent experiments, the cues were removed, and the setup was placed either in darkness or in a visually immersive virtual reality (VR) corridor. Throughout these experiments, fields of view were placed over anterior and posterior RSC subregions at stereotaxic coordinates ranging from –1.04 to –2.38 mm (anterior) and –2.32 to –4.17 mm (posterior) relative to bregma (Fig. 1B–C). Together, this enabled controlled characterization of visual and navigation-related signals across anterior and posterior subregions of RSCd.

Position coding varies along the anterior–posterior RSC axis
We first quantified single-neuron position selectivity across anterior and posterior RSC subregions using occupancy-normalized, deconvolved calcium activity and a shuffle-based statistical test (Fig. 2A, left, and Supplementary Fig. 1). For each neuron, we quantified the fraction of position-selective neurons, tuning reliability, peak response amplitude, tuning-field width, the number of fields, and spatial information (see Methods). We compared the anterior and posterior FOVs measured during treadmill locomotion with a uniform gray screen.

Position-tuned responses were present in both anterior and posterior RSC, but the prevalence of position-tuned neurons was higher in anterior RSCd (Fig. 2A, right; anterior vs. posterior: 45 ± 5 % vs. 21 ± 3 %; mean ± SEM; p = 0.0017; two-sided Mann–Whitney U test; n = 10 sessions per group from 8 animals, for pooled data statistical tests, see figure captions). Neurons in anterior RSC also exhibited sharper tuning (tuning field width: 45 ± 1.7 cm vs. 64.3 ± 4.2 cm; p = 0.002), carried more spatial information (0.28 ± 0.02 vs. 0.18 ± 0.01 bits/event; p = 3.5 x 10-9), and showed more reliable tuning (trial-to-trial correlation: 0.17 ± 0.02 vs. 0.11 ± 0.01; p = 0.017; Fig. 2B–F). Spatial information values computed from average tuning curves declined progressively along the anterior-posterior axis, particularly within anterior imaging RSC FOVs (Fig. 2C). The number of position fields per neuron was broadly similar across subregions, though anterior RSC FOVs contained a slightly higher fraction of multi-field neurons (Fig. 2G). Single-field responses dominated in both regions (56.4 ± 2.4% vs. 68.9 ± 6%; p = 0.10), while two-field responses were somewhat more common anteriorly (37.1 ± 1.7% vs. 27.9 ± 4.8%; p = 0.21). A small fraction of neurons displayed three fields, with a higher proportion in anterior RSC FOVs (6.4 ± 0.9% vs. 3.2 ± 1.9%; p = 0.006).

We next characterized population-level encoding of position across RSC subregions. Anterior RSC populations exhibited more pronounced sequential activity during treadmill running than posterior subregions (Fig. 3A–B, top). Bayesian decoding analysis revealed more robust representations in anterior RSC than in posterior RSC (mean decoding error: 5.2 ± 0.7 cm vs. 10.4 ± 1.2 cm; p = 0.0017; Fig. 3A–B, bottom; Fig. 3CA–D). Differences in response amplitudes were also observed, with neurons in anterior RSC showing higher response amplitudes during treadmill running than those in posterior RSC (mean peak amplitudes: 110 ± 4.1% vs. 95.9 ± 3.9%; p = 0.037; Fig. 3E). Taken together, single-cell and population analyses reveal systematic differences in position-related representations between anterior and posterior RSCd during treadmill locomotion.

Anterior and posterior RSC rely on different sensory cues for position coding
The encoding of position relies on integrating sensory information from multiple senses, including visual, somatosensory, and self-motion information. To determine whether anterior and posterior RSC subregions rely on similar sensory inputs to generate position-related activity, we assessed position tuning under targeted manipulations of tactile and visual inputs.

We first tested the contribution of somatosensory inputs to regional specialization by removing the tactile landmarks from the treadmill belt (Supplementary Fig. 2A–C; n = 8 anterior vs. 6 posterior sessions from 9 animals). Eliminating these somatosensory cues reduced the fraction of position-tuned neurons in anterior RSC (45 ± 5% to 35 ± 5%; mean ± SEM; p = 0.031), whereas the number of position-tuned cells in posterior RSC remained unchanged (21 ± 3% to 22 ± 7%; mean ± SEM; p = 0.79). This result suggests that anterior RSC relies more strongly on tactile inputs to maintain stable position representations.

We next examined the contribution of visual inputs to RSC responses by recording neural activity while animals ran on the treadmill in darkness (Supplementary Fig. 3; n = 9 sessions from 7 animals). Bayesian decoding analysis revealed that turning off ambient light reduced the reliability of position responses in both RSC subregions (average decoding error across subregions: 7.8 ± 0.9 cm to 15.7 ± 1.6 cm; p = 0.0039; n = 9 sessions, Supplementary Fig. 3A–B). However, the anterior–posterior gradient in responses persisted. Even in the absence of visual input, posterior RSC showed a trend toward larger decoding errors as compared with the anterior subregion (13.4 ± 0.7 cm vs. 20.1 ± 3.8 cm; p = 0.16, anterior vs. posterior, n = 6 vs. 3 sessions; Supplementary Fig. 3B). Posterior RSC also showed a lower fraction of position-tuned neurons as compared to anterior RSC (20 ± 2.5% vs. 7.6 ± 0.8%; p = 0.024; anterior vs. posterior, n = 6 vs. 3 sessions; Supplementary Fig. 3C). The reliability gradient of position-related activity was also partially maintained (Supplementary Fig. 3D–F). Thus, visual inputs influence position coding in both anterior and posterior RSC.

Finally, we asked whether engaging the animals in a visually-immersive VR task would differentially affect position coding in RSC subregions. To address this, we recorded RSC activity while animals navigated a VR corridor with salient visual landmarks (Supplementary Fig. 4A–B, n = 3 animals). After learning the task, as indicated by anticipatory licking near the reward zone (Supplementary Fig. 4C), both anterior and posterior RSC subregions exhibited clear single-cell position tuning and robust population representations of position (Supplementary Fig. 4D–E). Notably, posterior RSC showed improved tuning relative to that observed during navigation of the tactile-cued treadmill (Fig. 2A). After excluding neurons with peak responses near the end of the corridor (>200 cm), the proportions of position-tuned neurons in anterior and posterior RSC were comparable (44 ± 12% vs. 40 ± 8%; n = 3 sessions per group from 3 animals; Supplementary Fig. 4F–H).

Together, these results demonstrate distinct sensory dependencies along the anterior–posterior axis of RSC: anterior RSC relies more on tactile somatosensory cues, whereas posterior RSC exhibits enhanced position tuning in the presence of structured visual input. This modality-specific weighting suggests that the two subregions differentially integrate contextual information during navigation. To further characterize this anterior–posterior specialization, we next examined visually evoked responses independent of position coding.

Visual response properties differ between anterior and posterior RSC
The RSC receives input from both primary and higher-order visual cortex and displays visually evoked activity25,37. Furthermore, anterior and posterior subregions of the visual cortex show distinct visual tuning preferences38–41. To assess whether RSC subregions show visual specialization, we examined responses to visual stimulation. To assess the large-scale organization of visual responses across RSC, we performed widefield calcium imaging, which revealed a coarse retinotopic organization along the anterior–posterior axis: posterior RSC responded preferentially to stimuli in the nasal upper visual field, whereas anterior RSC showed weaker activation and was preferentially driven by stimuli in the nasal lower visual field (Supplementary Fig. 5; Supplementary Movie 1).

We next characterized the visual responses of RSC neurons at cellular resolution. To this end, we presented full-field drifting gratings spanning six orientations, twelve directions, three spatial frequencies (0.04–0.16 cpd), and two temporal frequencies (1 and 4 Hz) while the animals ran on the treadmill. Visually responsive neurons were defined by a median trial-to-trial correlation greater than 0.3 (Fig. 4A; Supplementary Fig. 6). Using this criterion allowed us to focus subsequent analyses on neurons with consistent visually evoked responses. Consistent with the widefield measurements, we observed cellular differences in visual responsiveness across RSC subregions. Posterior RSC contained a larger proportion of visually-responsive neurons and displayed higher visual reliability than anterior RSC (16.1 ± 2.7% vs. 33.8 ± 3.3%, p = 0.0017; reliability: 0.18 ± 0.04 vs. 0.26 ± 0.04, p = 0.0022; two-sided Mann–Whitney U test; n = 10 sessions per group from 8 animals; Fig. 4B–D).

We found clear differences in spatial and temporal frequency tuning across anterior and posterior RSC subregions. Anterior RSC neurons preferred low spatial frequency, high temporal frequency stimuli (e.g., 0.04 cpd at 4 Hz), whereas posterior RSC neurons were biased toward high spatial frequency, low temporal frequency stimuli (e.g., 0.16 cpd at 1 Hz; Fig. 5A–D). These differences were reflected in the distributions of neurons responsive to each SF/TF combination (e.g., 0.04 cpd at 4 Hz: 28.3 ± 3.5% anterior vs. 5.5 ± 1.1% posterior, p = 1.8 × 10-4; 0.16 cpd at 1 Hz: 6.5 ± 1.5% anterior vs. 25.0 ± 2.7% posterior, p = 3.3 × 10-4), and were robust to behavioral modulations (Supplementary Fig. 7). Notably, selectivity for orientation and direction did not differ across subregions (all p > 0.05; Fig. 5E–K).

In summary, visual signals are nonuniformly distributed across the RSC. Posterior RSC shows stronger, more reliable responses and preferential sensitivity to slow, high-resolution visual patterns, while anterior RSC is more responsive to fast, low-resolution motion. Notably, we found minimal overlap between neurons encoding visual and position signals (Supplementary Fig. 8A–D). These findings indicate that visual processing is organized along the anterior–posterior axis.

Anterior and posterior RSC subregions integrate specific sets of inputs
To determine whether this functional gradient is reflected in long-range anatomical input patterns, we performed brain-wide retrograde tracing. The retrosplenial cortex receives afferents from a broad set of cortical and subcortical regions, including visual cortex, the hippocampal formation (HPF), anterior thalamic nuclei (ATN), posterior parietal cortex (PTLp), and sensorimotor areas6,7,9,18,20,29,30,42,43. We therefore asked whether anterior and posterior RSC receive distinct patterns of input from these regions (n = 4 animals). We therefore mapped afferent projections to anterior and posterior RSC at cellular resolution using retrograde AAV tracers (Fig. 6A–C; Supplementary Table 3).

Injections targeted dorsal and ventral subdivisions of RSC at two stereotaxic coordinates: anterior (AP –1.5 mm) and posterior (AP –3.2 mm). In one animal, the posterior injection site was slightly shifted anteriorly (Fig. 6D). After histological processing, labeled neuronal cell bodies were registered to the Allen Mouse Brain Atlas44 (Fig. 6E). To quantify input distributions, we used two complementary measures: (1) volume-normalized cell density, which accounts for differences in source region size, and (2) fractional input, the proportion of labeled neurons from each region relative to the total input population.

Across the data set, the majority of RSC-projecting neurons originated from the cortex, comprising 80.7 ± 3.2% of inputs to anterior RSC and 71.9 ± 5.9% to posterior RSC (mean ± SEM, n = 4 animals; Fig. 6F–G). The hippocampal formation and thalamus (TH) also provided substantial inputs (HPF: 7.6 ± 1.6% and 12.4 ± 2.7%, TH: 6.1 ± 1.4% and 9.9 ± 3.3%, anterior vs. posterior. Fig. 6F–H). Thus, while overall input composition was broadly similar, notable quantitative differences emerged across regions.

Primary cortical sources included the anterior cingulate area (ACA), motor cortex (MO), somatosensory cortex (SS), posterior parietal cortex, and visual areas (VIS) (Fig. 6H and Fig. 7). ACA contributed similarly to both RSC subregions (Fig. 7A, anterior vs. posterior; 17.7 ± 2.3% vs. 19.8 ± 2.4%; mean ± SEM in fraction of inputs; n = 4 animals). In contrast, inputs from sensorimotor and parietal cortices were enriched in anterior RSC. MO, SS, and PTLp inputs were more abundant in anterior RSC (Fig. 7B–D, anterior vs. posterior; MO: 23.1 ± 2.7% vs. 7.1 ± 3.8%; p = 0.125; SS: 8.7 ± 1% vs. 1.2 ± 0.5%; PTLp: 4.9 ± 0.9% vs. 2.1 ± 0.5%). Posterior RSC received stronger input from visual cortices (Fig. 7E; anterior vs. posterior: 7.6 ± 2.1% vs. 23.7 ± 6.9%).

To further resolve differences within visual cortical inputs, we normalized projections from individual visual areas relative to the total VIS-derived input for each RSC subregion. Anterior RSC received proportionally stronger input from higher-order areas such as VISam (anterior vs. posterior: 33.6 ± 2.6% vs. 28.4 ± 6.6%) and VISpor (16.2 ± 7.2% vs. 3.8 ± 1.2%), whereas posterior RSC received more from primary (VISp, 20 ± 4.2% vs. 29.1 ± 5.3%) and posteromedial visual areas (VISpm, 17.2 ± 3.8% vs. 26.0 ± 0.6%). These anatomical biases may contribute to the distinct visual tuning profiles observed across subregions.

For hippocampal inputs (Fig. 8A), we focused our examination on the retrohippocampal region (RHP), which accounted for more than 90% of hippocampal afferents. Most labeled neurons (over 95%) were located in the subiculum (SUB) and entorhinal cortex (ENT). Although posterior RSC received more total RHP input, a greater proportion of the RHP input to anterior RSC originated from ENT (anterior vs. posterior: 51.7 ± 13.8% vs. 27.7 ± 6.7%). Subicular projections showed a clear dorsoventral gradient: dorsal SUB preferentially targeted anterior RSC, while ventral SUB targeted posterior RSC. This topographic organization aligns with functional specialization within the hippocampus, where dorsal SUB is associated with spatial processing.

Thalamic inputs were dominated by the anterior thalamic nuclei, which contributed more than 70% of total thalamic projections. Posterior RSC received a larger overall fraction of ATN input (7.8 ± 2.3% vs. 3.4 ± 0.6%; Fig. 8B). Within ATN, anterior RSC received a higher proportion from the anteromedial nucleus (AM: 52.0 ± 8.2% vs. 33.2 ± 3.3%), whereas posterior RSC received more from the anteroventral nucleus (AV: 46.7 ± 4.1% vs. 30.0 ± 4.6%). Unexpectedly, retro-AAV injections produced minimal labeling in the laterodorsal thalamic nucleus (LD), a major RSC afferent reported previously29,30. To determine whether this reflected a lack of connectivity or technical limitations, we performed control injections using cholera toxin subunit B (CTB), which yielded robust LD labeling (Supplementary Fig. 9A–B). Similarly, injecting the same retro-AAV construct into postsubiculum (a region known to receive LD input45) failed to label LD neurons (Supplementary Fig. 9C). These controls indicate that viral tropism, rather than absence of LD connectivity, accounts for the underrepresentation of LD inputs in our dataset.

Together, these results demonstrate anterior–posterior biases in long-range inputs to RSC that parallel functional differences identified in our imaging experiments (Fig. 8C–D).

Methods
Animals
All experimental procedures were approved by the Animal Ethics Committee of KU Leuven. For chronic cellular calcium imaging (see Supplementary Tables 1 and 2), we used either C57BL/6J-Tg(Thy1-GCaMP6s)GP4.12Dkim/J mice57 or CaMKII-tTA x TRE-GCaMP6s (line G6s2) mice58. Wild-type C57BL/6J mice were utilized for retrograde tracing experiments (Supplementary Table 3). All animals were between 3 and 8 months old at the time of the experiments. Mice were single-housed in an enriched environment consisting of cotton bedding, wooden blocks, and a running wheel, under a 12 h light/dark cycle, with room temperature maintained at 19–21°C and relative humidity ranging from 30% to 70%.

Resources
Non-animal resources used for this study are listed in Supplementary Table 4.

Cranial window surgery
To enable chronic cellular calcium imaging, mice were anesthetized with isoflurane (2.5–3% for induction, 1–1.25% for surgery). A custom-made titanium frame was attached to the skull, and a cranial glass window (4 or 5 mm, 2–3 layers) was implanted over the left hemisphere over the RSC59. Postoperative care included administration of Buprenex (0.6 mg/kg) and Cefazolin (15 mg/kg) every 12 h for two days to manage pain and prevent infection.

Habituation and behavioral training
Following recovery, mice were gradually habituated to head restraint while positioned on a linear treadmill. To motivate locomotion during the behavioral task, animals were placed on a water restriction schedule, receiving no less than 1 mL of water per day. Body weight was monitored daily to ensure it remained above 80% of their free-feeding weight.

Habituation progressed from short initial sessions to durations of up to 1 hour per day. During this period, animals were introduced to the experimental setup without tactile cues. A small water reward (~2–3 µL) was delivered at a fixed location following each completed lap. Additional rewards were occasionally provided to encourage task-related behaviors, such as licking and running. Once animals exhibited stable locomotion (> 5cm/s), they were transitioned to either the tactile treadmill task or the virtual reality task.

Tactile treadmill task
Two tactile cues (gray sponge or foam patches) were affixed to the treadmill belt at 50 cm and 100 cm positions. A small water reward (~2–3 µL) was delivered at a fixed location following each completed lap.

Virtual reality task
To examine position-related activity in a visually landmark-rich environment, locomotion was synchronized in real-time with the rendering of a virtual corridor15. The environment was displayed on a single monitor (Samsung 2233RZ; 1680 × 1050 resolution; 60 Hz refresh rate) covering ~120° of the frontal visual field, spanning the binocular zone of both eyes. The virtual track was 210 cm in length and consisted of two walls covered with randomized dot textures and a vertical grating landmark positioned at 100 cm. Upon reaching the end of the corridor, the visual scene terminated, and the animal was teleported to a reward zone (~2–3 µL water reward, 3s) to begin the subsequent trial. To minimize transient visual responses, all blocks were presented at the same mean luminance. In the open-loop condition (decoupled from locomotion), the virtual environment moved passively at ~6 cm/s.

Two-photon calcium imaging
During imaging experiments, eye movements and facial expressions were simultaneously recorded using an integrated tracking system with AVT Prosilica GC660 and Mako G-030 cameras, respectively. Cellular calcium imaging was conducted using a resonant scanner-based two-photon microscope (Neurolabware). In a subset of the experiments, an electrically tunable lens (ETL) was used to sequentially image across four optical planes, spanning a depth range of 60–300 µm, with an inter-plane spacing of 60–100 µm (see Supplementary Table 1). For single-plane imaging, data were acquired at ~30 frames per second, while ETL-based volumetric imaging achieved an effective frame rate of ~7.5 Hz per plane. The genetically encoded calcium indicator GCaMP6 was excited at 920 nm using a MaiTai DeepSee laser (Spectra Physics) through a 16x water-immersion objective (NA = 0.8, Nikon). The emitted green light was filtered (510/84 nm, Semrock) and detected using a GaAsP photomultiplier tube (PMT, Hamamatsu). Laser power output at the objective ranged from 20 to 100 mW, increasing with imaging depth. Images were acquired at a resolution of 794 × 527 pixels, corresponding to a physical area of 892 × 667 µm. To minimize contamination from ambient light, blackout material was used to shield the optical path from the visual display.

Single-photon calcium imaging
Widefield single-photon calcium imaging was used to map the retinotopic organization and delineate the boundaries of dorsal cortical areas. Excitation light was provided by a 470 nm blue LED (Thorlabs), and fluorescence emission was collected through a 510/84 nm green emission filter (Semrock) using a CCD camera (PCO Edge 3.1). Imaging was acquired at 10 frames per second using a 2x objective lens (NA = 0.10, Thorlabs), yielding an image resolution of 1024 × 768 pixels across the field of view.

Visual stimulation
Visual stimuli were presented on a calibrated 22-inch LCD monitor (Samsung 2233RZ, 1680 by 1050-pixel resolution, 60 Hz refresh rate), positioned 18 cm from the animal’s right eye. The screen covered 120° × 80° of the right visual field (0–120° azimuth; ±40° elevation). Custom software built in PsychoPy60 was used to control stimulus presentation and synchronize with imaging acquisition.

For retinotopic mapping, we presented a circular patch of flickering noise rotating counterclockwise along an elliptical trajectory centered on the display (Supplementary Movie 1). Each trial consisted of four rotations, followed by a 6s inter-trial interval, repeated across 30 trials.

For cellular calcium imaging, full-screen square wave drifting gratings of 12 directions and 6 combinations of spatial frequencies (0.04, 0.08, and 0.16 cpd) and temporal frequencies (1 and 4 Hz) were presented, covering the range of visual tuning observed in the visual cortex of awake mice38,39. Stimuli were presented in pseudo-randomized order at fixed 5s intervals with 3s of visual stimulation interleaved with 2s of gray screen (54–80 cd/m2, from center to edge of display). In dark-condition experiments, the monitor and all external light sources (e.g., for pupil or facial tracking) were turned off.

Viral vectors and CTB injections
To label neurons projecting to anterior and posterior RSC, retrograde AAV viral vectors were injected through a burr-hole craniotomy using a glass micropipette (30 µm tip diameter, Drummond Scientific Company, Pulling system from Sutter Instrument P-2000). Two combinations of viral vectors with identical promoters were used: AAVrg-CAG-tdTomato with AAVrg-CAG-GFP, or AAVrg-hSyn-mCherry with AAVrg-hSyn-GFP (titer: ≥ 7×10¹² vg/mL, Supplementary Tables 3 and 4). Injections were targeted using stereotaxic coordinates relative to Bregma. For the anterior RSC, the coordinates were anteroposterior (AP) -1.5 mm, mediolateral (ML) 0.5 mm, and dorsoventral (DV) -0.8 mm. For the posterior RSC, the coordinates were AP -3.2 mm, ML 0.5 mm, and DV -0.8 mm. Each injection was made approximately 0.5 mm below the cortical surface. A volume of 150–200 nL viral solution was infused at each site, using a Nanoject II injector that delivered ~13 nL per pulse at 20-second intervals.

To confirm thalamic input to RSC from the laterodorsal (LD) nucleus, we performed additional retrograde tracing using a non-viral method. A 0.5% solution of cholera toxin subunit B (C34775, ThermoFisher) was injected into either anterior or posterior RSC at the exact stereotaxic coordinates described above61.

Histology
Three weeks following viral injection (or two weeks after CTB injection), mice were deeply anesthetized with an intraperitoneal injection of ketamine and xylazine (100 mg/kg and 10 mg/kg, respectively), and transcardially perfused first with phosphate-buffered saline (PBS), followed by 4% paraformaldehyde (PFA). Brains were extracted and cut into 100 µm-thick coronal sections using either a vibratome (VT1000 S, Leica) or a cryostat (CM3050 S, Leica). Sections were mounted using Vectashield containing the nuclear stain DAPI (4',6-diamidino-2-phenylindole; Vector Laboratories). Confocal imaging was performed with a Zeiss LSM 900 microscope or Nikon Ti2 inverted microscope equipped with a 10x Plan-APOCHROMAT objective (NA = 0.45). Images were acquired using a 0.7x zoom setting with approximately 12 by 7 tile scans and a 15% overlap between tiles at a resolution of either 1.25 (1024 x 1024) or 2.5 µm (512 x 512) per pixel. Z-stacks were acquired with a step size of 5 - 7.5 µm and covered at least 80 µm in depth.

QUANTIFICATION AND STATISTICAL ANALYSIS
All data analyses were performed using custom scripts written in Python and MATLAB.

Widefield calcium imaging data
For widefield calcium imaging, fluorescence signals were first normalized to the pre-stimulation baseline. A min-max filter was applied to enhance spatial and temporal contrast (Supplementary Movie 1). Trial-averaged data were analyzed using a discrete Fourier transform along the temporal axis to extract pixel-wise frequency components, with amplitude representing response strength and phase indicating response timing. These components were visualized using an HSV colormap based on normalized amplitude and phase. Registration to the Allen Common Coordinate Framework (CCF) dorsal cortex was achieved by matching retinotopic maps in primary and higher visual areas44.

Cellular calcium imaging data
Cellular calcium imaging data were acquired using ScanBox and processed with Suite2p for motion registration, ROI detection, and calcium signal extraction62. The extracted neuronal activity included the raw fluorescence signal F and neuropil fluorescence signal F . For each ROI, the raw somatic cell np fluorescence signal was corrected by subtracting surrounding neuropil activity using the formula:
F = F - 0.7 × F
corr cell np
The baseline fluorescence was estimated using a min/max filter with 60s kernel size, applied to F , corr followed by Gaussian smoothing (σ = 10 time bins). Spike deconvolution was performed using the OASIS algorithm, which models the calcium trace as the convolution of spike events with an exponential decay kernel. A fixed decay constant (1.5s) appropriate for GCaMP6s was used63.

Selection of active neurons
To identify neurons actively engaged during the behavioral task, we applied separate criteria for movement-related and visually evoked activity. For task-related activity, lap reliability was calculated as the proportion of running laps in which a neuron exhibited significant calcium transients. Transients were considered significant if the fluorescence signal exceeded the baseline ΔF/F₀ by more than three times the standard deviation. This criterion ensured the inclusion of neurons that consistently responded across repeated laps.

For assessing visual responsiveness, trial-averaged ΔF/F₀ traces for each stimulus condition were baseline-corrected by subtracting the median pre-stimulus signal, calculated over the five frames preceding stimulus onset. To quantify the consistency of visually evoked responses, a visual reliability index (r) was computed as the 75th percentile of cross-trial Pearson correlation coefficients of the de-randomized response time courses. A neuron was classified as visually responsive if r > 0.339.

A neuron was considered active during the task if it exhibited either lap reliability greater than 0.3 (i.e., significant activity in at least 30% of laps) or visual reliability greater than 0.3.

Quantification of speed score
The speed score was calculated by the Pearson product-moment correlation between the cell’s instantaneous firing rate and the mouse’s instantaneous running speed, which scales from -1 to 164.

Population decoding of position
We employed a Bayesian algorithm65,66 to estimate the probability of position given the ∆F/F of a fixed 0 number of randomly selected neurons. Bayesian decoding was performed during running epochs, defined as a consecutive sequence of frames with forward movement lasting at least 1 second, with a minimum speed of 5 cm/s. Consecutive epochs separated by less than 0.5s were merged67.

To compare the decoding error between the anterior and posterior retrosplenial cortex, odd trials were used for model training, while even-numbered trials were reserved for testing. To assess decoding error in darkness, 80% of the total trials from the light-on session were used for training, and the remaining trials were reserved for testing using a 5-fold cross-validation approach. The Bayesian decoding equation is given by:
N
P(pos|a all ) = C(∏f i (pos)ai)e−τ∑N i=1 f i (pos)
i=1
where a represents the activity (∆F/F ) of the neurons, and C is the normalization constant ensuring all 0 that the posterior sums to 1. The parameter τ is the time bin size, which depends on the imaging sampling rate. The function f(pos)ai represents the position-binned activity (with 1.5 cm per bin), and N is the i number of neurons. The decoded position for each time bin was determined as the position with the maximum posterior probability. The decoding error was computed as the absolute difference between the actual position and the decoded position. Chance-level decoding was set at 50 spatial bins (75 cm).

Detection of neurons with position activity
Position-tuned neurons were identified using a lower-bound threshold computed from deconvolved calcium data68. The linear track was divided into 100 position bins (1.5 cm per bin), and the occupancy-normalized activity was smoothed using a Gaussian window (SD = 3 position bins) for each neuron. To determine statistical significance, neuronal activity was circularly shifted by a random time between 20s and the total session duration minus 20s, repeated 200 times to generate a shuffled distribution. A neuron was classified as position-tuned if the lower bound of its actual activity (mean-SEM) in any position bin across trials exceeded the 97.5th percentile of the shuffled distribution.

Position-tuned neurons were further examined by quantifying their position-tuning properties. The position tuning field width was calculated from the number of consecutive position bins in which the mean activity exceeded 30% of the difference between peak and baseline activity (below 15 cm or above 120 cm were discarded). The neuron was considered a position cell if it met the following criteria: (1) Position tuning field width was a continuous region spanning at least 15 cm but no more than 120 cm. (2) At least one position tuning field was presented in more than one-third of all trials.

Quantification of position-related activity
Spatial information (SI) was calculated using the following formula69:
N
f i f i
SIE= ∑p log ( )
i f 2 f
i=1
where p is the occupancy probability (the fraction of the time spent in the -th position bin), f is the i i occupancy-normalized deconvolved calcium activity (summed activity divided by the total time spent in the -th position bin), and f is the overall mean activity.

To examine activity reliability across trials (whether calcium transients were exhibited at the exact locations), we computed trial-to-trial correlation as the median of the pairwise Pearson correlation coefficients across trials using deconvolved calcium activity.

Characterization of direction and orientation visual selectivity
Orientation and direction tuning were quantified using the orientation selectivity index (OSI) and directional selectivity index (DSI), respectively:
OSI = Rp−Rortho
DSI = Rp−Rnull
Rp+Rortho Rp+Rnull
where R is the response to the preferred orientation/direction, R is the response to the orthogonal p ortho orientation, R is the response to the 180° direction from the preferred direction. Neurons with null OSI >0.33 or DSI >0.33 were considered orientation and direction-selective, respectively70.

Quantification of retrogradely labeled cells
Labeled neurons were manually selected in different channels separately. Registration was performed using the DAPI channel across all brain slices to the Allen Brain Atlas71. After applying a transformation matrix to the brain slice, each ROI was registered to a specific brain region based on the mouse brain atlas. ROIs were classified according to the hierarchy structure tree provided by the Allen Brain Institute44. A 3D whole-brain reconstruction with labeled ROIs was generated based on the model adapted from brainrender72.

To assess the relative input distribution between posterior and anterior subregions of the retrosplenial cortex (RSC), we computed a bias index defined as the log-ratio of the fractional input to posterior RSC versus anterior RSC:
f posterior
Bias Index = log ( )
2 f anterior
where f and f represent the fraction of input to the posterior and anterior RSC, respectively.
posterior anterior
Positive values indicate a relative bias toward posterior RSC, whereas negative values indicate a bias toward anterior RSC.

Quantification of fluorescence density
Fluorescence density was quantified from histological sections at the viral injection sites following standardized preprocessing and normalization steps. Images were smoothed to reduce high-frequency noise and normalized using intensity percentiles to correct for differences in overall brightness across sections. Background signal was estimated from non-labeled regions and subtracted, after which fluorescence values were rescaled to a standard intensity range. Each processed section was registered to the Allen Common Coordinate Framework (CCF), and the 90th percentile fluorescence density was visualized as contour plots.

Statistical testing
Statistical analyses were performed to assess differences in neuronal properties between anterior and posterior RSC across animals. To compare scalar summary measures, we used the two-sided Mann–Whitney U test, a non-parametric alternative to the t-test that does not require normality assumptions. For population-level comparisons, we used the two-sample Kolmogorov–Smirnov test, which evaluates differences in both the shape and central tendency of distributions. Statistical significance levels are reported as follows: p < 0.05 (*), p < 0.01 (**), p < 0.001 (***).

Data availability
All two-photon imaging datasets (Suite2p-processed) and retrograde-labeling data (CSV format) have been deposited in Zenodo and are publicly available (https://doi.org/10.5281/zenodo.17639283). Any additional data supporting the findings of this study are available from the corresponding author upon request.

Code availability
All custom code used in this study is openly available on GitHub (https://github.com/ytsimon2004/rscvp) and archived in Zenodo (https://doi.org/10.5281/zenodo.18234118). The public repository includes detailed documentation and Google Colab notebooks to facilitate streamlined data download and full reproduction of the analyses. Further information required to reproduce or reanalyze the results is available from the corresponding author upon request.

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "This manuscript investigates multimodal information encoding and long‐range input connectivity along the rostro‐caudal axis of retrosplenial cortex (RSC), revealing gradients in spatial and visual processing and connectivity. The topic is timely, as systematic subregion analyses in RSC are rare, and the data could substantially advance our understanding of RSC’s role in sensory, spatial, and mnemonic functions. However, several issues must be addressed to support the authors’ conclusions for publication.\nMajor comments:\n1- Rostro-caudal spatial gradient vs. sensory drive: The observed position‐related activity may reflect multimodal integration of somatosensory, visual, and self‐motion cues, rather than pure “spatial” coding. Considering the use of tactile cues as landmark on the treadmill, the stronger rostral position-related signals may arise from stronger somatosensory inputs to the rostral, as compared to caudal, RSC rather than a generalised higher spatial content. Indeed, the viral tracing data in Figure 6H suggest stronger somatosensory cortical inputs to rostral RSC. Indeed, the viral tracing data in Figure 6H suggest stronger somatosensory cortical inputs to rostral RSC. To conclude that the observed difference reflects spatial coding per se, the study could test position-related signals with non-tactile landmarks (visual cues and running alone) to show that the gradient persists, or temper the conclusion to acknowledge that stronger rostral coding could arise from greater somatosensory and motor inputs, as supported by the tracing data.\n2- Underestimated thalamic input: The use of retrograde viral tracing and whole-brain quantification of input connectivity provides a rich and informative dataset that complements the functional data. However, there is a major concern regarding thalamic connections: the results show minimal laterodorsal (LD) nucleus input, which contradicts extensive literature identifying the LD as one of the key thalamic afferents to the RSC. Since this discrepancy may stem from viral tropism, I suggest validating the results with an alternative viral or structural retrograde tracing method before drawing firm conclusions about connectivity.\n3- Sampling across RSC layers and subregions: Since a major finding of the study is rostro-caudal gradient in spatial and visual information processing, it is important to demonstrate similar sampling across the subregions – granular (gRSC) vs dysgranualr (dRSC) areas – and cortical layers (2/3 vs 5) along the rostro-caudal axis. For instance, rostro-caudal differences in cortical layer depth and dRSC/gRSC boundaries may lead to oversampling from gRSC in more rostral regions or oversampling of layer 5 in more caudal regions (300 µm depth is well within layer 5 in caudal RSC). This issue applies to both functional and anatomical data, and can be addressed by showing the laminar and regional distribution of imaged neurons and viral injection spread for both rostral and caudal sections to demonstrate similar sampling.\n4- Subicular and ENT input, position coding, and Figure 7G-H: The discussion section suggests that the position-related signal in RSC is hippocampal in origin. If so, one would predict that rostral RSC’s stronger position coding should coincide with stronger input from subiculum (the hippocampus’s primary output). Instead, Figure 8 shows a posterior bias in subicular input for 3/4 animals (positive bias index). Similarly, although the text states that entorhinal cortex (ENT) input is stronger to anterior RSC, the mean ENT bias index is near zero, and individual animals have both positive and negative biases. It is unclear how the indices in Figure 7H align with the stacked bar chart in Figure 7G, since the panel captions lack detail.\nThe manuscript also describes a dorsal–ventral gradient in subicular projections to anterior vs. posterior RSC, yet no per-animal or group-level quantification is provided. If Figure 7G illustrates this gradient, please explain the panel in the caption to define all colours and results clearly (it’s very difficult to understand this panel as is), and include statistics (mean ± SEM, test values, p-values) to support any regional differences.\nOverall, the claim that rostro-caudal differences in position coding are explained by parahippocampal connectivity is not fully supported, because i) the gradient may reflect sensorimotor integration rather than hippocampal-dependent spatial coding (see comment 1); ii) it is unclear whether subiculum itself exhibits a comparable dorsal–ventral gradient in position coding (please cite any relevant studies); iii) a significant difference in dorsal vs. ventral subicular input to RSC has not been convincingly demonstrated.\nAdditional comments:\n• Powell et al. (2020) demonstrated a rostro-caudal gradient in visual responses in RSC. Please discuss and contrast their findings in the discussion section on visual properties. The text currently implies that no prior study has explored this question in the context of spatial and visual processing and that prior knowledge about rostro-caudal organization was limited to object recognition and contextual fear (lines 421–427).\n• Page 3, line 91: The description of imaged areas is unclear and does not match Figure 1C. The text describes imaging “in RSPd within the agranular layer of the RSC at depths of 60–360 µm below the pial surface.” Dysgranular and agranular RSC are two separate but adjacent subregions. Additionally, Figure 1C shows the FOV covering both dysgranular (RSCd) and part of granular RSC (referred to as RSCv in this manuscript). Finally, based on the Allen Brain Atlas, this imaging depth covers both layer 2/3 and layer 5 (especially in caudal RSC, as explained above). Please clarify the imaging location with details about layers and subregions in both rostral and caudal RSC and resolve the inconsistency between the text and Figure 1C.\n• For all statistical analyses, neurons across all animals are pooled and treated as independent replicates. This inflates N and raises the risk of type I errors (small effects may appear significant even if between-animal variance is large). I recommend either collapsing each animal’s anterior and posterior responses into per-animal summary statistics and testing across animals, or fitting a mixed-effects model with animal as a random effect.\n• Two different transgenic mouse lines are used in this study. What was the rationale, and are the results consistent between these two lines?\n• Page 12, lines 306-309: Please include SEM values.\n• Page 13, lines 318 -322, lines 330- 335, lines 339 – 342: Statistics are missing in all these sections that conclude differences between anterior and posterior RSC connectivity.\n• Page 13, line 328: revise “anterior dorsal” to “anterior” thalamic nuclei (ATN)", "reviewer_2": "In this manuscript, Wei et al. investigate differences in connectivity and functional responses between anterior and posterior dysgranular retrosplenial cortex (dRSC). They do some convincing two-photon imaging experiments to show that anterior and posterior dRSC show differences in visual and allocentric spatial tuning. They also do some nice histology using retrograde viral tracing to quantify differences in connectivity. These findings, though perhaps of somewhat specialized interest, would mkae a nice contribution to the field. However, I do have a few concerns about specific analyses and their scholarship, as described below:\nMajor:\n1. The scholarship is not very thorough when discussing differences in anterior and posterior connectivity. For example, in the introduction they cite papers that have nothing to do with anterior-posterior gradients of visual/hippocampal input (e.g., Niell and Stryker, 2008, Goldbach et al., 2021, etc.), while skimming over or missing papers of very direct relevance. For example, Yamawaki et al., J Neuro, 2016 and Powell et al, Cereb Cortex, 2020 (both cited elsewhere in the manuscript, but not here) both offer direct evidence of anatomical and functional gradients along anterior-posterior RSC. The authors also completely missed Franco & Goard, Science Advances, 2021, which show both anatomical and functional gradients in anterior-posterior RSC using similar techniques.\nAs a result, the authors present certain results—such as stronger visual tuning in posterior dRSC—as being novel, despite having been convincingly shown in previous publications (Powell et al, 2020; Franco & Goard, 2021).\n2. The authors argue that posterior RSC shows “a clear retinotopic organization” (line 202), but I could not see a clear retinotopic organization in Figure S3. Most of the mice only showed visual responses to a single retinotopic location, and certainly nothing like the retinotopic maps seen in Powell et al., 2020. The authors should clarify and explain any discrepancies with prior work.\n3. There are a few topics I wish the authors would have covered in the Discussion. First, are the differences between anterior and posterior RSC just due to connectivity, or are there additional cytoarchitectural differences? Should we be considering dRSC to be a single structure or two separate structures? Second, the authors consider allocentric spatial tuning, but is there any reason to think that egocentric tuning (e.g., Alexander et al., 2020; van Wijngaarden et al., 2020) might show differences across A-P RSC?\nMinor:\n1. Throughout the paper, the author’s often talk about anterior-posterior differences in RSC, but their data really only covers dysgranular RSC, it is not clear that the A-P gradient of responses applies to granular RSC. The authors should make this clear.\n2. For waterfall plots in Figure 2B, please plot cross-validated data (i.e., use half the data to find the preferred spatial location for each cell, and the other half for plotting the response). Also, make sure to describe any sorting in the figure legend.\n3. I found the plots of Figure 6E and Figure 7A-D hard to read. I would encourage the authors to make the colors more different (e.g., green and magenta) so the difference/overlap is more clear.", "reviewer_3": "The study by on the retrosplenial cortex (RSC) in mice, focusing on its role in visuospatial processing and navigation. The study combines 2-photon calcium imaging and brain-wide retrograde circuit mapping to investigate the functional and anatomical organization of the RSC. The research reveals that the RSC has distinct anterior-posterior gradients in terms of neural activity and connectivity, which support specialized processing of visual and spatial information. The findings indicate that the RSC is organized in a modular fashion, with different subregions contributing complementary information during navigation. The study provides insights into the circuitry underlying multimodal integration in the RSC, which is crucial for spatial orientation and cognitive processes.\nI like the brain-wide mapping of projectivity to dissociate the functional independence/modularity of anterior and posterior RSC. However, the functional characterization of different parts of RSC has several weaknesses. If the authors were able to consolidate the functional aspects of the study, I think this would be a very important paper, and a good candidate for the journal.\n1) The biggest issue lies in the behavioral paradigm. While the study integrates two distinct behavioral paradigms to simultaneously assess visual and spatial functions, it lacks sufficient exploration of the potential interactions between these paradigms when probing the respective functional representation of anterior and posterior RSC.\nSpatial cognition depends on multiple sensor modalities. However, when assessing the spatial coding properties, the task only involved tactile landmarks on the belt. Is it likely that the weaker spatial properties of posterior RSC neurons would not hold if visual landmarks were also present? If so, then the conclusion would be different from what has been portrayed in the current manuscript.\n2) In addition, the authors should characterize the running speed of the animals when presenting the visual stimulus. If the animals were able to move, there would be a discrepancy between the visual update and self-movement. It is possible that there are differential sensitivity to this discrepancy between the anterior and posterior parts of RSC.\n3) The authors should comment on how the current results can be reconciled with earlier studies (e.g., Trask et al. cited in the current paper). How can the different visual-spatial functions and projectivity patterns explain the different roles of anterior and posterior RSC in memory?\n4) The authors should acknowledge that Powell et al., 2020 (Figure 1) also mentioned the posterior RSC was more visual."}, "author_response": "Summary of Major Revisions\nIn response to reviewer concerns, we performed substantial new experimental work and\nstrengthened multiple analyses:\n1. New functional experiments\nTo determine whether tactile cues drive the anterior–posterior spatial gradient, we added:\n– A non-tactile treadmill condition, showing that the gradient persists without textured cues.\n– A visually immersive VR condition, demonstrating position tuning across modalities and\nrevealing enhanced posterior position coding under visual-dominant conditions.\nThese results are presented in Supplementary Figures 2 and 4.\n2. Validation of thalamic inputs using independent tracers\nBecause retro-AAV under-labeled the laterodorsal (LD) nucleus, we performed new CTB\nretrograde tracing and additional AAV injections into postsubiculum, confirming that LD input\nis strong but poorly labeled by retro-AAV. These data are now included in Supplementary\nFigure 9, and LD-related quantification has been revised accordingly.\n3. Clarification of laminar and subregional sampling\nWe quantified imaging depth and coverage across dorsal and ventral RSC, demonstrating\npredominant sampling within layer 2/3 of dorsal RSC. Figure 1C has been updated with an\nimproved anatomical schematic, and the text now clearly limits functional claims to dorsal RSC.\n4. Refined analyses of parahippocampal input\nWe added quantification of dorsal versus ventral subiculum projections to anterior vs posterior\nRSC, clarified how entorhinal inputs were normalized, and substantially revised our\ninterpretation to emphasize modality-dependent integration rather than hippocampal\ndominance.\n5. Updated retinotopy and figure improvements\nWe replaced earlier retinotopic plots with phase maps (Supplementary Figure 5), updated\nseveral figures for clarity (Figures 6–8), and applied animal-level statistical tests across all\nfunctional comparisons.\n6. Expanded Discussion\nWe incorporated reviewer-requested considerations regarding cytoarchitectural gradients,\negocentric coding, and methodological differences relative to prior literature.\nReviewer #1 (Remarks to the Author):\nThis manuscript investigates multimodal information encoding and long‐range input\nconnectivity along the rostro‐caudal axis of retrosplenial cortex (RSC), revealing\ngradients in spatial and visual processing and connectivity. The topic is timely, as\nsystematic subregion analyses in RSC are rare, and the data could substantially\nadvance our understanding of RSC’s role in sensory, spatial, and mnemonic functions.\nHowever, several issues must be addressed to support the authors’ conclusions for\npublication.\n1. Rostro-caudal spatial gradient vs. sensory drive: The observed position‐related\nactivity may reflect multimodal integration of somatosensory, visual, and self‐motion\ncues, rather than pure “spatial” coding. Considering the use of tactile cues as landmark\non the treadmill, the stronger rostral position-related signals may arise from stronger\nsomatosensory inputs to the rostral, as compared to caudal, RSC rather than a\ngeneralised higher spatial content. Indeed, the viral tracing data in Figure 6H suggest\nstronger somatosensory cortical inputs to rostral RSC. Indeed, the viral tracing data in\nFigure 6H suggest stronger somatosensory cortical inputs to rostral RSC.\nWe agree that the position-related activity observed in RSC likely reflects the\nintegration of multiple sensory modalities, including somatosensory, visual, and self-\nmotion cues, rather than purely abstract spatial coding. We now clarify in the revised\ntext that the stronger position tuning observed in anterior RSC may be driven in part\nby denser somatosensory and motor inputs, as supported by our retrograde tracing\ndata and previous study (Lande et al., 2023). We have revised the Discussion to better\nreflect this interpretation (Line 269–287).\nTo conclude that the observed difference reflects spatial coding per se, the study could\ntest position-related signals with non-tactile landmarks (visual cues and running alone)\nto show that the gradient persists, or temper the conclusion to acknowledge that\nstronger rostral coding could arise from greater somatosensory and motor inputs, as\nsupported by the tracing data.\nTo directly address this point, we now include new data from two complementary\nparadigms designed to isolate the contribution of somatosensory input.\nFirst, we analyzed recordings from sessions in which animals ran on a blank treadmill\nwithout textured cues. In this tactile-deprived condition, the overall fraction of position-\ntuned neurons in anterior RSC was reduced, but the anterior–posterior gradient in\nposition coding remained. This suggests that somatosensory input enhances, but does\nnot fully account for, the stronger spatial coding observed in anterior RSC. These data\nare now presented in Supplementary Figure 2.\nSecond, we recorded RSC activity in a visually immersive virtual reality (VR)\nenvironment. Under these conditions, posterior RSC exhibited robust position tuning,\nwhile anterior RSC remained strongly responsive. This indicates that the anterior–\nposterior differences in position coding are not exclusively dependent on\nsomatosensory input and can be shaped by the dominant sensory modality. These\nfindings are now presented in Supplementary Figure S4 and discussed in a new\nsection of the Discussion (Line 269–287).\nTogether, these results support the view that anterior and posterior RSC subregions\nare differentially engaged depending on the available sensory cues, and that the\nanterior–posterior gradient in position coding reflects flexible, modality-dependent\nintegration rather than a simple hierarchical encoding of “spatialness.” We have\ntempered our conclusions accordingly and clarified this interpretation throughout the\nmanuscript.\n2. Underestimated thalamic input: The use of retrograde viral tracing and whole-brain\nquantification of input connectivity provides a rich and informative dataset that\ncomplements the functional data. However, there is a major concern regarding\nthalamic connections: the results show minimal laterodorsal (LD) nucleus input, which\ncontradicts extensive literature identifying the LD as one of the key thalamic afferents\nto the RSC.\nSince this discrepancy may stem from viral tropism, I suggest validating the results\nwith an alternative viral or structural retrograde tracing method before drawing firm\nconclusions about connectivity.\nWe thank the reviewer for raising this important point. Upon re-examining our dataset,\nwe confirmed that retrograde AAV injections into both anterior and posterior RSC\nresulted in only sparse labeling of cell bodies within the laterodorsal (LD) thalamic\nnucleus. Instead, the observed fluorescence signal in LD primarily reflected passing\naxonal fibers from RSC projection neurons, rather than retrogradely labeled somata\n(Figure 8B; see also representative images from all animals in Reviewer Figure 1\nbelow).\nTo evaluate whether this underrepresentation reflects a limitation of the viral tracer,\nwe conducted additional control experiments. First, injections of the non-viral\nretrograde tracer cholera toxin subunit B (CTB) into the RSC resulted in robust LD\nlabeling, consistent with previous anatomical studies and confirming strong LD → RSC\nconnectivity. Second, we injected the same retro-AAV construct (pAAV-CAG-GFP)\ninto the postsubiculum, a region also known to receive dense LD input. These\ninjections similarly failed to produce substantial LD labeling, further suggesting that\nthe lack of labeling reflects viral tropism or transport inefficiency rather than the\nabsence of anatomical connectivity. We have added these new control data as\nSupplementary Figure 9, removed LD, IAM, and IAD data from the main figure and\nquantification (Figure 8B), and explicitly acknowledged this limitation in the Results\nsection (Line 239–249).\nReviewer Figure 1. Retrograde AAV projection patterns from ATN to RSC\nExample confocal images show two coronal slices per animal. Slice 1 (more anterior)\nand Slice 2 (more posterior) illustrate the sparse labeling in LD (yellow areas)\n3. Sampling across RSC layers and subregions: Since a major finding of the study is\nrostro-caudal gradient in spatial and visual information processing, it is important to\ndemonstrate similar sampling across the subregions – granular (gRSC) vs dysgranualr\n(dRSC) areas – and cortical layers (2/3 vs 5) along the rostro-caudal axis. For instance,\nrostro-caudal differences in cortical layer depth and dRSC/gRSC boundaries may lead\nto oversampling from gRSC in more rostral regions or oversampling of layer 5 in more\ncaudal regions (300 µm depth is well within layer 5 in caudal RSC). This issue applies\nto both functional and anatomical data, and can be addressed by showing the laminar\nand regional distribution of imaged neurons and viral injection spread for both rostral\nand caudal sections to demonstrate similar sampling.\nWe thank the reviewer for raising this important point regarding potential sampling\nbiases across RSC layers and subregions. We have taken several steps to address\nthis concern in both our anatomical and functional datasets.\nFor the anatomical tracing experiments, we now quantify the\nnormalized fluorescence density surrounding each injection\nsite to assess the spatial extent of viral expression (Figure\n6D). In both anterior and posterior injection sites, viral\nspread encompassed both dorsal and ventral RSC (RSCd\nand RSCv). However, we note that in one animal (Animal\n#2), the posterior injection site was slightly anteriorly\ndisplaced, resulting in lower labeling from visual cortical\nareas.\nTo assess laminar and subregional sampling in our two-photon imaging experiments,\nwe estimated the anatomical locations of imaged neurons by aligning optical planes\nacross sessions to a standardized depth reference. We approximated the first imaging\nplane to be ~50 µm below the pial surface (corresponding to ~300 µm below Bregma),\nwith an inter-plane spacing of ~80 µm. Based on these estimates, the majority of\nrecorded neurons in RSCd were located in layer 2/3—approximately 80% in anterior\nfields of view and 70% in posterior—while the remaining neurons were distributed in\ndeeper layers. Overall, we estimate that ~80–90% of functionally imaged neurons\nwere in RSCd, with ~10–20% in RSCv and no sampling of RSCagl. However, given\nthe limitations of indirect anatomical registration, exact laminar and subregional\nassignments remain approximate.\nWe now address these sampling considerations for both the functional and anatomical\ndatasets in the revised Discussion (Lines 304–318).\n4. Subicular and ENT input, position coding, and Figure 7G-H: The discussion section\nsuggests that the position-related signal in RSC is hippocampal in origin. If so, one\nwould predict that rostral RSC’s stronger position coding should coincide with stronger\ninput from subiculum (the hippocampus’s primary output). Instead, Figure 8 shows a\nposterior bias in subicular input for 3/4 animals (positive bias index). Similarly,\nalthough the text states that entorhinal cortex (ENT) input is stronger to anterior RSC,\nthe mean ENT bias index is near zero, and individual animals have both positive and\nnegative biases. It is unclear how the indices in Figure 7H align with the stacked bar\nchart in Figure 7G, since the panel captions lack detail.\nThe manuscript also describes a dorsal–ventral gradient in subicular projections to\nanterior vs. posterior RSC, yet no per-animal or group-level quantification is provided.\nIf Figure 7G illustrates this gradient, please explain the panel in the caption to define\nall colours and results clearly (it’s very difficult to understand this panel as is), and\ninclude statistics (mean ± SEM, test values, p-values) to support any regional\ndifferences.\nOverall, the claim that rostro-caudal differences in position coding are explained by\nparahippocampal connectivity is not fully supported, because i) the gradient may\nreflect sensorimotor integration rather than hippocampal-dependent spatial coding\n(see comment 1); ii) it is unclear whether subiculum itself exhibits a comparable\ndorsal–ventral gradient in position coding (please cite any relevant studies); iii) a\nsignificant difference in dorsal vs. ventral subicular input to RSC has not been\nconvincingly demonstrated.\nRole of parahippocampal input in position coding\nWe agree that anterior–posterior differences in position coding cannot be attributed\nsolely to parahippocampal connectivity. In the revised Discussion (Line 269–287), we\nemphasize that multimodal integration may plays a role in shaping position-tuned\nresponses in RSC, particularly in the anterior subregion. This view is supported by\nrecent studies (e.g., Gianatti et al., 2023; Lande et al., 2023), which demonstrated that\nbrain areas such as M2 and PPC convey robust position-related signals, especially in\ntactile-rich environments.\nRegarding hippocampal connectivity, prior work has established a dorsal–ventral\nfunctional gradient within the subiculum: dorsal subiculum (the main output of dorsal\nCA1) is implicated in spatial and cognitive processing, while ventral subiculum (output\nof ventral CA1) is more involved in emotional and stress-related functions (Fanselow\n& Dong, 2010). Spatially selective cells—such as boundary vector cells (Lever et al.,\n2009) and geometry-sensitive neurons (Sun et al., 2024), are preferentially located in\ndorsal subiculum. In our retrograde tracing experiments, we observed that while the\noverall fraction of subicular inputs was slightly higher for posterior RSC, clear\ntopographic pattern were observed: dorsal subiculum preferentially targets anterior\nRSC, whereas ventral subiculum sends stronger projections to posterior RSC. To\nsupport this interpretation, we now provide quantification, statistics, and representative\nconfocal images in the Figure 8A.\nClarification of ENT input and figure interpretation\nWe appreciate the reviewer’s comment regarding the interpretation of entorhinal\ncortex (ENT) input. Although the average bias index across animals is close to zero,\nwe find that when ENT inputs are normalized within the hippocampal/parahippocampal\ngroup, a greater proportion is directed to anterior RSC. To clarify this distinction and\navoid confusion, we have revised the results, figure legend and improved the visual\npresentation to better illustrate the data.\n- Horizontal bar plots show the fraction of inputs from each subregion,\nnormalized by the total number of labeled neurons across all subregions within\nthat region (e.g., across all parahippocampal subregions). This provides a\nmeasure of the percentage contribution of each subregion to RSC inputs (e.g.,\n~50% of inputs from the parahippocampal region to anterior RSC arise from\nENT)\n- Vertical bar plots and bias indices are calculated relative to the total input\npopulation, capturing the absolute proportion of each input source (e.g., ENT\naccounts for ~3% of the total inputs to anterior RSC).\nTogether, these complementary analyses convey both subregional specificity and\noverall distribution biases.\nRegarding statistics, due to the limited sample size (n = 4), we report mean ± SEM\nand show paired data across animals, but do not perform formal statistical\ncomparisons.\nAdditional comments:\n• Powell et al. (2020) demonstrated a rostro-caudal gradient in visual responses in\nRSC. Please discuss and contrast their findings in the discussion section on visual\nproperties. The text currently implies that no prior study has explored this question in\nthe context of spatial and visual processing and that prior knowledge about rostro-\ncaudal organization was limited to object recognition and contextual fear (lines 421–\n427).\nWe revised Discussion section (Line 288–302) to contrast our observations with those\nPowell et al. (2020).\nPage 3, line 91: The description of imaged areas is unclear and does not match\nFigure 1C. The text describes imaging “in RSPd within the agranular layer of the RSC\nat depths of 60–360 µm below the pial surface.” Dysgranular and agranular RSC are\ntwo separate but adjacent subregions. Additionally, Figure 1C shows the FOV\ncovering both dysgranular (RSCd) and part of granular RSC (referred to as RSCv in\nthis manuscript). Finally, based on the Allen Brain Atlas, this imaging depth covers\nboth layer 2/3 and layer 5 (especially in caudal RSC, as explained above). Please\nclarify the imaging location with details about layers and subregions in both rostral and\ncaudal RSC and resolve the inconsistency between the text and Figure 1C.\nAs noted in our response to point 3, we have revised Figure 1C by removing “RSCv”\nlabel and adding a sagittal schematic to illustrate the estimated imaging depth,\nproviding clearer context for laminar coverage. These changes are now reflected in\nthe main text and updated figure legend\nFor all statistical analyses, neurons across all animals are pooled and treated as\nindependent replicates. This inflates N and raises the risk of type I errors (small effects\nmay appear significant even if between-animal variance is large). I recommend either\ncollapsing each animal’s anterior and posterior responses into per-animal summary\nstatistics and testing across animals, or fitting a mixed-effects model with animal as a\nrandom effect.\nMost statistical comparisons of neuronal activity across conditions (e.g., Figures 2D–\n2G) were already performed at the animal level, using per-animal means as\nindependent observations (Line 746–752). Visual response reliability (Figure 4B) and\ndirection selectivity index (Figure 5F) now follow the same procedure, ensuring\nconsistency across all metrics. These revisions ensure that all reported statistical tests\nindicating significance are now based on animal-wise comparisons, rather than\ntreating individual neurons as independent replicates. This approach mitigates the risk\nof inflated Type I errors and more appropriately accounts for between-animal variability.\nTwo different transgenic mouse lines are used in this study. What was the rationale,\nand are the results consistent between these two lines?\nThe initial experiments were performed in the Thy1-GCaMP6s line which was used in\nour observation of position-related activity in mouse RSC (Mao et al 2017). We also\nrecorded from CaMKII-tTA × TRE-GCaMP6s which provides distinct sampling and\nbroader coverage of excitatory neurons. Despite differences in expression density,\nboth lines exhibited the same anterior–posterior trends in position-related and visually\nevoked responses. Because our key comparisons were performed within animals,\npotential between-line variability was minimized. To increase transparency, we revised\nthe figures with animal-wise quantification to display the two lines using distinct color\ncodes (Thy1: gray; CaMKII: black).\nPage 12, lines 306-309: Please include SEM values.\nWe have added the corresponding SEM values (fraction of cortical RSC-projecting\nneurons)\nPage 13, lines 318 -322, lines 330- 335, lines 339 – 342: Statistics are missing in all\nthese sections that conclude differences between anterior and posterior RSC\nconnectivity\nGiven the limited sample size (n = 4 animals), we decided not to perform formal\nstatistical tests for these connectivity comparisons, as they would be underpowered\nand potentially misleading. Instead, we present mean ± SEM values and connect\npaired data points across animals in the figures to allow readers to visually assess\nconsistency across animals.\nPage 13, line 328: revise “anterior dorsal” to “anterior” thalamic nuclei (ATN)\nWe have revised the terminology in the main text to “anterior thalamic nuclei (ATN)”\nto align with common convention. In the figure captions for Figures 6–8, we retained\nthe term “anterior group of the dorsal thalamus” to remain consistent with the\nnomenclature used in the Allen Mouse Brain Atlas (Wang et al., 2020).\nReviewer #2 (Remarks to the Author):\nIn this manuscript, Wei et al. investigate differences in connectivity and functional\nresponses between anterior and posterior dysgranular retrosplenial cortex (dRSC).\nThey do some convincing two-photon imaging experiments to show that anterior and\nposterior dRSC show differences in visual and allocentric spatial tuning. They also do\nsome nice histology using retrograde viral tracing to quantify differences in connectivity.\nThese findings, though perhaps of somewhat specialized interest, would make a nice\ncontribution to the field. However, I do have a few concerns about specific analyses\nand their scholarship, as described below:\nMajor:\n1. The scholarship is not very thorough when discussing differences in anterior and\nposterior connectivity. For example, in the introduction they cite papers that have\nnothing to do with anterior-posterior gradients of visual/hippocampal input (e.g., Niell\nand Stryker, 2008, Goldbach et al., 2021, etc.), while skimming over or missing papers\nof very direct relevance. For example, Yamawaki et al., J Neuro, 2016 and Powell et\nal, Cereb Cortex, 2020 (both cited elsewhere in the manuscript, but not here) both\noffer direct evidence of anatomical and functional gradients along anterior-posterior\nRSC. The authors also completely missed Franco & Goard, Science Advances, 2021,\nwhich show both anatomical and functional gradients in anterior-posterior RSC using\nsimilar techniques.\nAs a result, the authors present certain results—such as stronger visual tuning in\nposterior dRSC—as being novel, despite having been convincingly shown in previous\npublications (Powell et al, 2020; Franco & Goard, 2021).\nWe thank the reviewer for this valuable feedback. We have revised the Introduction to\nmore accurately situate our study within prior work on anterior–posterior organization\nof RSC (Line 47–57). In addition, we have tempered our novelty claims: the\nmanuscript now explicitly acknowledges that stronger visual responsiveness in\nposterior RSC has been reported previously (Powell, 2020; Franco & Goard, 2021).\nOur contribution lies in extending these findings by (i) jointly comparing spatial and\nvisual coding within the same animals across anterior vs posterior RSC, (ii) identifying\na novel spatiotemporal tuning gradient (posterior: fine/slow motion; anterior:\ncoarse/fast motion), (iii) testing modality dependence with non-tactile, dark, and VR\nlandmark–rich conditions, and (iv) linking these functions to brain-wide input mapping.\n2. The authors argue that posterior RSC shows “a clear retinotopic organization” (line\n202), but I could not see a clear retinotopic organization in Figure S3. Most of the mice\nonly showed visual responses to a single retinotopic location, and certainly nothing\nlike the retinotopic maps seen in Powell et al., 2020. The authors should clarify and\nexplain any discrepancies with prior work.\nTo enhance the visualization of retinotopic organization in the RSC, we have updated\nSupplementary Figure 5 to display phase maps, which depict each pixel’s preferred\nstimulus location independent of response amplitude. Additionally, we now include a\nsupplementary video to illustrate how retinotopic signals extend from lateral visual\nareas into the RSC. Across most animals, we observed a consistent spatial pattern in\nwhich retinotopic responses followed a posterolateral-to-anteromedial axis,\ncorresponding to the upper-to-lower nasal visual field. In two animals (#3 and #7), this\npattern was less distinct, which may reflect their reduced locomotor activity during\nimaging. This is consistent with findings from Powell et al. (2020), who reported that\nvisual responses in RSC neurons are enhanced during periods of increased\nlocomotion.\nPowell et al. also described azimuthal gradients of cellular responses in the posterior\nRSC, which were less apparent in our widefield imaging data. This discrepancy may\nstem from methodological differences: Powell et al. employed drifting bar gratings\noptimized for continuous retinotopic mapping, whereas we used discrete circular patch\nstimuli, which are less sensitive to fine azimuthal gradients. Additionally, widefield\nimaging captures population-level activity, potentially masking the finer retinotopic\nstructure revealed by cellular-resolution approaches (Line 288–302).\n3. There are a few topics I wish the authors would have covered in the Discussion.\nFirst, are the differences between anterior and posterior RSC just due to connectivity,\nor are there additional cytoarchitectural differences? Should we be considering dRSC\nto be a single structure or two separate structures? Second, the authors consider\nallocentric spatial tuning, but is there any reason to think that egocentric tuning (e.g.,\nAlexander et al., 2020; van Wijngaarden et al., 2020) might show differences across\nA-P RSC?\nWe thank the reviewer for these insightful points. We now address both in the revised\nDiscussion: cytoarchitectural considerations and functional gradients in RSC (Lines\n304–318), and the potential for egocentric coding differences along the anterior–\nposterior axis (Lines 320–329).\nMinor:\n1. Throughout the paper, the author’s often talk about anterior-posterior differences in\nRSC, but their data really only covers dysgranular RSC, it is not clear that the A-P\ngradient of responses applies to granular RSC. The authors should make this clear.\nWe agree with the reviewer and have clarified this point in the revised Discussion\n(Lines 304–318). To illustrate the subregional and laminar sampling more clearly, we\nalso added an estimated depth distribution of imaged neurons in Figure 1C,\nhighlighting that our functional data primarily reflect dorsal RSC (RSCd)\n2. For waterfall plots in Figure 2B, please plot cross-validated data (i.e., use half the\ndata to find the preferred spatial location for each cell, and the other half for plotting\nthe response). Also, make sure to describe any sorting in the figure legend.\nWe thank the reviewer for the helpful suggestion. For the population trial-averaged\nactivity plots (Figure 2B, Supplementary Figure 2A–B, Supplementary Figure 4D),\nwe now apply a cross-validation procedure: one half of the trials is used to determine\neach neuron’s preferred spatial location for sorting, and the other half is used to plot\nthe heatmap. The persistence of the diagonal pattern in these cross-validated plots\nconfirms that the observed spatial tuning is robust and not an artifact of sorting. The\nfigure legends have been updated accordingly to clarify this analysis.\n3. I found the plots of Figure 6E and Figure 7A-D hard to read. I would encourage\nthe authors to make the colors more different (e.g., green and magenta) so the\ndifference/overlap is more clear.\nWe have improved the visualization in Figures 6 and 7 (now are Figures 6–8) by\nsimplifying the color coding to better match the physiological data and by enhancing\nthe contrast between groups. In Figure 7, we further increased clarity by splitting the\nreconstructions into anterior versus posterior projections and by replacing the stacked\nbar chart with horizontal bar plots. These changes make the overlap and regional\ndifferences easier to interpret\nReviewer #3 (Remarks to the Author):\nThe study by on the retrosplenial cortex (RSC) in mice, focusing on its role in\nvisuospatial processing and navigation. The study combines 2-photon calcium\nimaging and brain-wide retrograde circuit mapping to investigate the functional and\nanatomical organization of the RSC. The research reveals that the RSC has distinct\nanterior-posterior gradients in terms of neural activity and connectivity, which support\nspecialized processing of visual and spatial information. The findings indicate that the\nRSC is organized in a modular fashion, with different subregions contributing\ncomplementary information during navigation. The study provides insights into the\ncircuitry underlying multimodal integration in the RSC, which is crucial for spatial\norientation and cognitive processes.\nI like the brain-wide mapping of projectivity to dissociate the functional\nindependence/modularity of anterior and posterior RSC. However, the functional\ncharacterization of different parts of RSC has several weaknesses. If the authors were\nable to consolidate the functional aspects of the study, I think this would be a very\nimportant paper, and a good candidate for the journal.\n1. The biggest issue lies in the behavioral paradigm. While the study integrates two\ndistinct behavioral paradigms to simultaneously assess visual and spatial functions, it\nlacks sufficient exploration of the potential interactions between these paradigms when\nprobing the respective functional representation of anterior and posterior RSC.\nSpatial cognition depends on multiple sensor modalities. However, when assessing\nthe spatial coding properties, the task only involved tactile landmarks on the belt. Is it\nlikely that the weaker spatial properties of posterior RSC neurons would not hold if\nvisual landmarks were also present? If so, then the conclusion would be different from\nwhat has been portrayed in the current manuscript.\nWe thank the reviewer for this important point. To directly address this concern, we\nperformed an additional set of experiments in which mice navigated a visually\nimmersive virtual reality (VR) environment. In this task, salient visual landmarks were\nthe dominant spatial cues, in contrast to the tactile cued belt paradigm used previously.\nThese new data, now included in Supplementary Figure 4, show that posterior RSC\nexhibits robust position-related activity in the presence of visual landmarks, with a\nclear sequence of spatial tuning across neurons. Notably, anterior RSC continued to\nshow strong spatial coding in the VR task, consistent with its broader integrative role.\nHowever, compared to the tactile condition, posterior RSC position coding was\nsignificantly enhanced under visually enriched conditions, supporting the idea that its\nspatial engagement is more dependent on visual context.\nWe now explicitly incorporate these results into the revised manuscript. In the\nDiscussion, we highlight this distinction, emphasizing that anterior RSC encodes\nspatial information across both tactile and visual conditions, while posterior RSC is\nmore selectively recruited when structured visual input is available. This suggests that\nposition coding in RSC is shaped by the dominant sensory modality, and that anterior\nand posterior subregions contribute differentially depending on sensory context.\nThese findings strengthen the central message of the manuscript: that the anterior-posterior\naxis of RSC exhibits distinct patterns of multimodal integration, rather than\nuniform spatial representation across tasks. We have revised the text throughout the\nResults and Discussion to clarify this point and avoid overgeneralization from the\ntactile paradigm alone.\n2. In addition, the authors should characterize the running speed of the animals when\npresenting the visual stimulus. If the animals were able to move, there would be a\ndiscrepancy between the visual update and self-movement. It is possible that there\nare differential sensitivity to this discrepancy between the anterior and posterior parts\nof RSC.\nTo address this point, we have included a new supplementary figure (Supplementary\nFigure 8) quantifying both the animals’ running behavior and RSC population neural\nactivity during visual stimulation. Animals exhibited a slight reduction in running speed\nduring visual stimulus presentation, particularly in response to temporal-to-nasal\nmotion. This suggests that visual motion can influence ongoing locomotor behavior,\npotentially reflecting a mismatch between visual flow and self-motion cues.\nTo assess whether such mismatch impacts neural activity, we analyzed the amplitude\nof population calcium signals (ΔF/F₀) across sessions for opposing visual motion\ndirections. As shown in Supplementary Figure 7C, we did not observe consistent\ndifferences in response amplitude across matched stimulus orientations, indicating\nthat visual-motor mismatch does not strongly modulate overall RSC population activity.\nTo further determine whether self-motion alone might account for responses attributed\nto visual stimulation, we computed a speed score for each neuron—defined as the\ncorrelation between instantaneous deconvolved activity and the animal’s running\nspeed (Kropff et al., 2005; Supplementary figure 7D). We identified both positively and\nnegatively speed-modulated neurons in both anterior and posterior RSC. The\ndistribution of speed scores was slightly broader in anterior RSC, consistent with our\nanatomical tracing results showing stronger motor-related inputs to this region.\nWe next assessed the overlap between speed-modulated and visually responsive\nneurons. As shown in Supplementary Figures 7E–F, the two populations were largely\nnon-overlapping, indicating that visual and locomotor signals are encoded by distinct\nneuronal subpopulations within RSC.\nTogether, these results suggest that although visual stimuli can influence running\nbehavior, this does not lead to systematic changes in population response amplitude.\nFurthermore, the minimal overlap between speed- and visually modulated neurons\nsupports the conclusion that visually evoked activity in RSC is not confounded by self-\nmotion signals at the single-cell level.\n3. The authors should comment on how the current results can be reconciled with\nearlier studies (e.g., Trask et al. cited in the current paper). How can the different\nvisual-spatial functions and projectivity patterns explain the different roles of anterior\nand posterior RSC in memory?\nWe thank the reviewer for highlighting this connection. While our study focuses on\nvisuospatial processing rather than memory per se, we agree that our findings provide\na useful framework for interpreting prior work on RSC’s role in memory. For instance,\nTrask et al. found that posterior RSC is required for encoding and retrieval of\ncontextual memories, whereas anterior RSC is more involved when integrating context\nwith event-related information. This aligns with our observation that posterior RSC is\nmore visually driven and context-sensitive, while anterior RSC is more integrative and\nposition-reliable. We have added a brief note on this in our response to reviewers but\ndid not expand it in the main text due to the differing behavioral paradigms.\n4. The authors should acknowledge that Powell et al., 2020 (Figure 1) also mentioned\nthe posterior RSC was more visual.\nIn revised Discussion, we explicitly state that our observation of stronger visual\nresponses in posterior RSC is consistent with Powell et al. (2020), who also reported\nenhanced visual responsiveness in posterior RSC."}, {"round": 2, "reviewer_comments": {"reviewer_1": "Thank you for addressing my comments and questions in this revised version. I am happy with the changes made to text and figures and additional data.\nI only have one minor comment – please check that all figure panels are clearly described in the figure legend. For instance, there is no reference to the vertical plots in Figure 8A.", "reviewer_2": "The authors have made a number of improvements to their manuscript and have addressed all of my concerns. I support publication.", "reviewer_3": "I thank the authors for the additional experiments and analyses performed, which have significantly improved the manuscript. I have no further comments."}, "author_response": "Reviewer #1:\nThank you for addressing my comments and questions in this revised version. I am happy\nwith the changes made to text and figures and additional data.\nResponse: We thank the reviewer for their positive evaluation of our revisions.\nI only have one minor comment – please check that all figure panels are clearly described\nin the figure legend. For instance, there is no reference to the vertical plots in Figure 8A.\nResponse: We thank the reviewer for catching this omission. We have carefully reviewed\nall figure legends to ensure that every panel is clearly described. We have revised the\nFigure 8A legend to include a description of the vertical bar plots showing density and\nfraction of inputs.\nReviewer #2:\nThe authors have made a number of improvements to their manuscript and have\naddressed all of my concerns. I support publication.\nResponse: We thank the reviewer for their support and positive assessment of our\nmanuscript.\nReviewer #3:\nI thank the authors for the additional experiments and analyses performed, which have\nsignificantly improved the manuscript. I have no further comments.\nResponse: We thank the reviewer for their thorough evaluation and for acknowledging\nthe improvements made to the manuscript."}]

Decision letter