The claustrum enhances neural variability by modulating the responsiveness of the prefrontal cortex

Domain: physics
Published: 2025-11-28
Editorial outcome: major_revision

Abstract
The claustrum is a densely interconnected structure involved in cognitive functions, but its influence on prefrontal circuits remains unclear. We conducted two-photon calcium imaging to assess mice dorsal prefrontal cortex (dPFC) cell responses during exposure to visual stimuli and widefield photostimulation of claustrum axons embedded in the dPFC. We identified three distinct subpopulations of neurons—sensory responsive, opto responsive, and opto-boosted cells—each exhibiting unique response dynamics to combined visual and optogenetic stimuli. Our findings reveal that claustrum stimulation increased single-cell variability while aligning average responses across neurons, thereby enhancing network homogeneity. During Pavlovian training, enhanced variability persisted, but homogeneity increased further, suggesting experience-dependent refinement. Finally, we performed claustrum axon silencing experiments which revealed that the claustrum may operate bidirectionally to maintain enhanced variability and homogeneity in the dPFC. These results highlight the crucial role of the claustrum in dynamically modulating dPFC activity, impacting both neuronal variability and network synchronization.

Introduction
The claustrum and the prefrontal cortex (PFC) are tightly interconnected through a robust anatomical network1–3, hinting at a pivotal role for the claustrum in modulating executive functions governed by the PFC4–10. While lesioning studies have not identified a single dysfunction associated with the claustrum, studies have unveiled intriguing insights into the claustrum’s involvement in a wide range of tasks11–15.

These broad functions are supported by the claustrum’s extensive anatomical connectivity. It receives input from and projects to widespread cortical regions, including prefrontal, cingulate, temporal, and retrohippocampal areas2, and shows modality-specific topography in its connections with sensory and association cortices8,16–18. This connectivity underlies its proposed role as a hub for coordinating cortical dynamics during cognitively demanding states. Recent studies have shown that the claustrum contributes to multimodal integration16,19, reward association and decision-making20,21 and the coordination of cortical slow-wave activity22,23. It also facilitates contextual memory24–26 and plays a critical role in performance under high cognitive load27,28. These findings converge on a view of the claustrum as a key regulator of cortical communication and internal state transitions.

Building on this emerging view of the claustrum as a regulator of cortical dynamics, recent studies have begun to dissect the mechanisms by which it modulates cortical activity. One proposal is that the claustrum contributes to cognitive control by synchronizing distributed cortical networks29,30. Supporting this idea, the claustrum activation has been observed across multiple behavioral tasks that require cognitive control, including attention allocation, engagement restriction and task switching10,31,32. Mechanistically, claustrum activation can bidirectionally modulate cortical excitability depending on brain region, cell type, and behavioral state. In frontal cortex, brief claustrum stimulation preferentially recruits fast-spiking interneurons, leading to a net inhibitory effect9, whereas prolonged stimulation can result in complex excitation–inhibition patterns across the brain. Whole-brain mesoscale imaging further reveals that reducing claustrum activity enhances PFC responses and sensory-evoked activity, while increasing claustral output suppresses frontal responses and alters global functional connectivity33. These findings underscore the claustrum’s capacity to shape neural dynamics both locally and across distant brain areas.

However, a key unresolved question is how the claustrum influences activity in the PFC: does it exert its effects directly via monosynaptic projections, or indirectly by engaging other cortical or subcortical regions that, in turn, modulate PFC function? This distinction is fundamental for understanding the organizational logic of claustro-cortical communication and the specificity of its cognitive impact. Given the claustrum’s widespread connectivity, isolating its direct effects on PFC circuits requires a pathway-specific approach. In this study, we address this by selectively stimulating claustrum axons within the PFC, enabling us to probe the functional consequences of direct claustro-PFC input while minimizing polysynaptic recruitment of other brain regions. This approach allows us to directly investigate how claustral input modulates prefrontal activity. Supporting this strategy, recent anatomical and optogenetic evidence has demonstrated that a subset of claustrum axons projecting to the PFC exhibit robust light-evoked responses16, providing a foundation for targeted, circuit-specific manipulation.

Building upon this foundation, our study aims to elucidate the claustrum’s contribution to PFC neural responsiveness, exploring its influence in both naive and trained states. To probe the functional consequences of this pathway, we combined local claustrum axon photostimulation embedded within the PFC with passive visual stimulation while recording PFC population activity using two-photon calcium imaging. We leveraged the known sensory responsiveness of dPFC neurons34,35 and used passive visual stimuli to avoid confounding effects of task demands, motivation, or motor output—factors known to interact with both claustrum and PFC circuits32. This approach enabled a controlled examination of how claustral input shapes local PFC computation.

Our results demonstrate that claustrum input dynamically regulates dPFC responsiveness to sensory cues, acting as a bidirectional modulator that can either enhance or suppress sensory processing. This modulation serves to enhance neural variability and promote network homogeneity. The magnitude and direction of this effect depend on the baseline excitability of individual neurons and are shaped by prior experience, suggesting a learning-dependent gating of claustral influence. These findings highlight the claustrum’s role in fine-tuning sensory processing and cognitive function, enabling the PFC to adaptively respond to internal states and external demands.

Results
Claustrum axon photostimulation alters dPFC cell responsiveness
We employed an optogenetic experimental framework to elucidate how claustrum axons modulate responses in the dorsal prefrontal cortex (dPFC), (Fig. 1A). We monitored neural activity in dPFC in mice during exposure to visual stimuli, while photostimulating claustrum axons in the same area. Genetically modified mice expressed the calcium indicator GCaMP6s in CaMKII+ cells and the red-shifted opsin ChrimsonR36 in claustrum axons (Fig. 1B, C, see methods for our injection strategy, Supplementary Fig. 1). We employed two-photon microscopy in head-fixed mice for population calcium imaging. The mice were exposed to visual stimuli (“visual”), photostimulation (“opto”), and a combination of both visual stimuli and photostimulation (“visual+opto”) in various inter-trial intervals (Fig. 1D; 30 trials each, randomly presented, visual stimuli: 590nm, 56lm high-brightness LED, total duration 250ms; photostimulation: 595nm, 10×25ms pulses @ 40Hz, at 1.5mW, total duration 250ms). To prevent unintended retinal activation from stray photostimulation light, we incorporated a custom-designed light blocker around the objective. When the light blocker was in place, optogenetic stimulation did not induce pupil constriction, confirming that visual pathways were not inadvertently engaged (Supplementary Fig. 2). This setup facilitated the observation of neural dynamics at the single-cell resolution across a large field of view (580×580µm) as we administered wide-field photostimulation (cranial window: ~4000µm diameter) to claustrum axons, thereby assessing the influence of claustrum axon photostimulation on dPFC cellular responses to visual stimuli.

Calcium responses revealed diverse changes in dPFC neurons following claustrum axon photostimulation. Some neurons exhibited enhanced responses to visual+opto stimuli (Fig. 1E), while others showed reduced responses under the same conditions (Fig. 1F). Notably, these effects were observed in both excited and inhibited responses (Supplementary Fig. 3A, B), highlighting the high diversity of the claustrum axons’ impact on dPFC neuronal activity to visual stimuli.

A subset of neurons exhibited activity exclusively in response to optostimuli, showing no discernible reaction to visual stimuli alone or when presented in combination with photostimulation (Fig. 1G). On the other hand, a distinct subset of cells demonstrated an interesting phenomenon where photostimulation effectively enhanced their responsiveness to visual stimuli, even though these cells did not exhibit any response when presented with visual stimuli or photostimulation alone (Fig. 1H, and Supplementary Fig. 3C). These findings show the complex and precise modulation of neuronal responses in the dPFC by claustrum axon photostimulation, highlighting its role in selectively enhancing or suppressing neural activity under varying condition.

Upon analyzing all the recorded cells, we found that 49% of the total recorded cells exhibited responsiveness (Fig. 1I, n=24,575 from 49,212 recorded cells, n=13 mice, 270 field of view) Responsiveness defined by an independent t-test comparing activity 1 sec before and after stimulus offset (−1 to 0s vs. stimulus duration to stimulus duration+1s) for visual, opto, or visual+opto trials, with false discovery rate correction applied for multiple comparisons (α=0.05). Within this responsive cohort, 21% showed responsiveness to visual stimuli (n=10,531) and were classified as sensory responsive cells, with 42% of these cells being exclusively responsive to visual stimuli. 29% responded to opto stimuli (n=11,365) and were classified as opto responsive cells, among which 44% were exclusively responsive to opto stimuli. 3% of these cells (n=1802) exhibited responsiveness to either visual or opto stimuli alone but not to visual+opto stimuli. Furthermore, 14% of the cells (n=5783), were classified as opto-boosted cells, and displayed responsiveness to visual stimuli only when presented with photostimulation, indicating a possible interaction between the visual stimuli and photostimulation that is not evident when each stimulus is presented alone.

To provide a complete picture of population-level response types, we also identified several overlapping categories—such as cells responsive to both visual and opto stimuli, or to all three conditions (visual, opto, and visual+opto). While each of these combinations represented fewer than 8% of cells, they are reported in Supplementary Table 1 for transparency. However, we emphasize that the interaction among these categories is complex and not always easily interpretable — for example, cells responsive to visual and opto stimuli independently may reflect convergence or unrelated excitability, without clear synergy. For this reason, we focused our main analyses on the three most dominant and mechanistically informative groups: sensory-responsive, opto-responsive, and opto-boosted neurons.

To better understand how these combined subpopulations within the same network process visual and opto stimuli, we examined the balance between excited (E) and inhibited (I) responses, which is crucial for neural network information processing37,38. The E:I ratio refers to the proportion of cells that exhibited a significant increase (excitation) versus a significant decrease (inhibition) in ΔF/F response following stimulus presentation. This analysis revealed that opto-responsive cells uniquely exhibited a higher prevalence of inhibited responses, with an E:I ratio of 33:67 (0.49, p=0.004, n=13 mice, Fig. 1L). When considering only opto responsive cells that were exclusively responsive to optogenetic stimulation (excluding any cells responsive to visual or visual+opto stimuli), the E:I ratio became even more distinct 28:72 (0.39).

For comparison, sensory-responsive cells exhibited an E:I ratio of 48:52 (0.92, Fig. 1K), and opto-boosted cells showed an E:I ratio of 44:56 (0.79, Fig. 1M). Notably, the trend toward a higher proportion of inhibited responses was unique to the opto-only group and was not observed in the sensory-responsive or opto-boosted populations. The elevated inhibition in opto-responsive cells aligns with prior findings6 and support the interpretation that optogenetic activation of claustral axons can drive inhibitory dynamics within the dPFC network—particularly in cells not otherwise responsive to sensory input. In contrast, the lack of a similar inhibitory bias in the sensory or opto-boosted groups may reflect either more subtle effects of inhibition or the dominance of excitatory drive from visual stimuli. These results suggest that inhibition is a context-dependent component of the broader modulatory influence of claustrum input within dPFC circuits.

The claustrum modulates dPFC neuronal responses to enhance neural flexibility
To clarify the role of three identified subpopulations in the dPFC during claustrum axon photostimulation, we performed targeted analyses focusing on their individual response patterns to better understand their functional dynamics in sensory processing.

Initially, we focused on sensory responsive cells and, consistent with the literature, observed varying visual cues responses among dPFC cells34,35,39, displaying different levels of excitation and inhibition (Fig. 2A). Upon visual+opto stimulation (Fig. 2B), these cell responses notably changed (Fig. 2C), with excited responses in cells that were originally inhibited by visual stimuli alone and inhibited responses in those that were excited. To quantify this effect, we calculated the mean ΔF/F for 1500ms after stimulation. The resulting histogram for visual+opto stimuli showed a significantly different distribution, where photostimulation shifted the bimodal distribution toward a Gaussian-like curve, indicating a normalising effect on neuronal responsiveness (Fig. 2D). At the population level, the absolute magnitude of neuronal responses to visual stimuli significantly decreased in the presence of photostimulation (Fig. 2E, p<0.001, Kolmogorov-Smirnov test). To better understand how the distribution of neuronal responses was affected, we performed an empirical cumulative distribution function analysis, which revealed that a greater number of neurons were responsive when visual+opto stimuli were presented compared to visual stimuli alone (Supplementary Fig. 4A). This pattern suggests that, although the amplitude of individual responses decreased, the optogenetic intervention recruited a larger population of neurons, thereby enhancing distributed sensory processing with a balanced level of responsiveness.

To explore the mechanisms underlying this distributed processing, we hypothesized that the claustrum might increase neural flexibility in the dPFC by elevating the variability in neuronal responses across repeated trials. We initially quantified this variability by examining the coefficient of variation, which showed a significant increase under visual+opto stimulation (Fig. 2F, p<0.001, paired t-test). We then applied dynamic time warping (DTW) analysis—a technique that measures the similarity between two temporal signals—across trials for both visual and visual+opto stimuli. A higher DTW value indicates a greater disparity between two sequences, necessitating more adjustments to achieve alignment. Significantly larger DTW values for the visual+opto condition (Fig. 2G, p<0.001, paired t-test) demonstrated increased variability when visual cues were combined with optostimulation, suggesting that the neurons are adapting their firing patterns more distinctly in each trial. This findings support our hypothesis that claustral modulation enhances neural flexibility by increasing the variability of neuronal responses within the dPFC.

Subsequent analysis focused on the opto-boosted subpopulation, a unique subpopulation that did not respond to visual or optogenetic stimuli alone but showed activity when these stimuli were combined. The response distribution of these neurons shifted from a non-responsive state to a bimodal pattern of activation upon visual-optogenetic stimulation, as illustrated by histograms (Fig. 2H–K). Notably, there was a significant increase in the absolute magnitude of responses to visual-opto stimuli compared to visual stimuli alone (Fig. 2I, p<0.001, Kolmogorov-Smirnov test). This suggests that photostimulation can unveil latent responsiveness in certain dPFC neurons, by making the neurons more excitable. This subpopulation also exhibited a higher coefficient of variation under visual+opto stimuli (Fig. 2J, p<0.001, paired t-test) and more pronounced temporal variability in dynamic time warping analyses (Fig. 2K, p<0.001, paired t-test) highlighting an enhanced flexibility in their response patterns when visual stimuli presented with optostimulation.

Although opto-responsive neurons do not show changes in the absolute magnitude of their response to visual cues (Fig. 2M, p=0.192, paired t-test), they also exhibited increased variance in their neural response for visual+opto stimuli (Fig. 2N, p<0.001, paired t-test) and DTW analysis (Fig. 2O, p<0.001, paired t-test).

Despite the differing results observed in the first two subpopulations — where sensory-responsive cells exhibit normalized responses and opto-boosted cells reveal latent responsiveness — these outcomes collectively suggest that claustrum stimulation in the dPFC enhances the flexibility of neural responses. The observed increase in the coefficient of variation and pronounced temporal variability indicates greater variability in neuronal responses across all subpopulations when subjected to combined visual and optogenetic stimuli. Collectively, these findings suggest that the claustrum plays a crucial role in facilitating flexible and distributed processing mechanisms within the dPFC.

Greater dPFC network homogeneity with claustrum axon modulation
The greater response variation we observed in these subpopulations was consistent across all animals as well as in non-responsive cells (Fig. 3A-C), suggesting that optogenetic stimulation broadly affects the neural network beyond just the most noticeable responsive cells. This implies a widespread influence of the claustrum on the dPFC, potentially affecting even those neurons that do not typically react strongly to visual and/or optogenetic stimuli alone.

To assess the claustrum modulation on the dPFC neural network, we first examined the opto modulation compared to the neural response to visual cues. Correlation analysis revealed a strong negative correlation. As the ΔF/F response to visual stimuli increased, photostimulation had a more pronounced inhibitory effect. In contrast, when the ΔF/F response was negative, photostimulation led to an excitatory effect across all subpopulations (sensory: Fig. 3D, r=−0.876, p<0.001; opto: Fig. 3E, r=−0.851, p<0.001; opto-boosted: Fig. 3F, r=−0.735, p<0.001). Hence, claustrum modulation in dPFC cells strongly depends on the baseline state of responsiveness of the given cell, and this dependency is not limited to excitatory cells.

Additional experiment revealed that inhibitory cells exhibit a similar pattern of modulation, indicating that this effect was consistent across different cell types (Supplementary Fig. 5, n=5 Nkx2.1- GCaMP6s transgenic mice). Furthermore, activating thalamic axons in the same region did not reproduce the variability or opto-boosted effects observed with claustrum stimulation, suggesting that the modulation was not a general feature of all subcortical inputs (Supplementary Fig. 6, n=3 CamKII-GCamP6s transgenic mice).

Then, we assessed the collective impact of all responsive cells including the three identified subpopulations by examining the cross-correlation of trial-averaged responses across all neurons. To capture global changes in representational structure, we first computed a representational similarity analysis (RSA) across all recorded neurons from all animals (Fig. 3G). The matrix for visual stimuli showed distinct cell group clustering, suggesting a highly coordinated neural network (Fig. 3G, left). Conversely, the matrix for visual + opto stimuli demonstrated a more uniform distribution, with an absence of distinct clustering (Fig. 3G, right, using the same clustering as defined by the visual condition), indicative of a more homogenized representational landscape.

To further examine, we calculated within-FOV cross-correlation of trial-averaged responses for 1.5sec after the stimulation window to quantify local network synchrony. This analysis revealed that optogenetic stimulation increased the average pairwise correlation within each FOV (Fig. 3I), consistent with a synchronization of activity across the recorded population.

When we calculated the average change in correlation due to optostimulation across animals, both the global RSA (which summarizes representational similarity across all recorded neurons) and within-FOV cross-correlation showed a consistent increase for all responsive as well as non-responsive cells (Fig. 3H, significantly higher in all responsive cells than non-responsive cells, p<0.001).

Notably, while the coefficient of variation analysis indicated higher variability in responses across trials within individual cells, the cross-correlation analysis, which examines trial-averaged responses, suggests increased synchrony among dPFC neurons during visual + opto trials. This indicates that the visual + opto stimuli facilitated a more homogenized and correlated interaction among all cells. This synergy among subpopulations likely contributes to refining dPFC activity, strengthening functional connections between neurons, and integrating them into a more cohesive network.

Claustral modulation-induced network homogeneity is enhanced by experience
To investigate how claustral modulation affects neural flexibility driven by experience, we trained mice (n=8) in a Pavlovian conditioning task with the same visual stimuli. Mice were water-restricted and presented with the same visual cues as in the experiments above followed by a drop of water (80% of the trials). 7 mice showed anticipatory licking activity before the reward within 10 days (Fig. 4B–D, p=0.028), indicating that they had learned to associate the stimuli with the reward. By first establishing this association, we ensured that the visual stimuli acquired behavioral relevance, thereby engaging cortical circuits of interest such as the claustrum and PFC, even during subsequent passive exposure40,41. This approach allowed us to probe neural representations of behaviorally meaningful stimuli without the confounds of motor output or ongoing task performance. After mice learned the association, they underwent the previous recording protocol in which they were passively exposed to visual cues with and without photostimulation. Notably, in this recording, the animals were familiar with visual stimuli although it was passively presented.

Similar to the naive animal recordings, we found three subpopulations in similar ratios for the trained animal recordings (53% responsive cells, 22% sensory responsive cells, 30% opto boosted cells and 12% opto-boosted cells, Fig. 4E). However, we observed no significant differences in excitatory and inhibitory responses among opto responsive cells, unlike the findings from the naive dataset (Fig. 4F, sensory: p=0.578; opto p=0.110; opto-boosted p=0.297, pairwise t-test). These three subpopulations displayed similar patterns post-training; (i) sensory-responsive neurons exhibited reduced response magnitude with an increased disparity in neural response upon claustrum axon photostimulation (Fig. 4G), (ii) opto-boosted neurons showed amplified response magnitudes alongside greater neural variability (Fig. 4H), and (iii) optogenetically responsive neurons displayed consistent absolute magnitudes but demonstrated enhanced variance in neural responses (Fig. 4I).

The fundamental mechanisms across subpopulations remained consistent; the change in absolute magnitude (Fig. 4J, sensory p=0.031; opto p=0.110; opto-boosted p=0.297, paired t-test), the change in the coefficient of variation (Fig. 4K, sensory p=0.219; opto p=0.375; opto-boosted p=0.578, paired t-test), and the change in DTW (Fig. 4L, sensory p = 0.0782; opto p = 0.469; opto-boosted p=0.812, paired t-test) was greater than zero but did not differ in trained animals compared to naive animals across populations. This suggests that while training influences how visual cue perceived and behavioral responses, the intrinsic variability in neural responses induced by photostimulation remains relatively stable. Hence, the modulatory effects of the claustrum on these neurons are robust against experiential changes—at least for the aspects of neural response variability measured here.

On the other hand, correlation analysis revealed an average increase in correlation among neuronal responses in trained animals, indicating a more synchronized and integrated network response following training (Fig. 4M). A two-way ANOVA showed significant main effects of training (F(1,24)=112.57, p<0.001) and responsiveness (F(1,24)=64.40, p<0.001). Additionally, there was a significant interaction between training and responsiveness (F(1,24)=12.86, p = 0.002), indicating that the effects of training varied depending on cell responsiveness. This implies that experience amplifies the effects of claustrum axon stimulation, leading to more coordinated neural activity, particularly in responsive cells, while preserving the underlying mechanisms.

Silencing the claustrum also enhances neural flexibility in the dPFC
To further elucidate the functional role of the claustrum, we conducted an additional photostimulation experiment to explore the effects of claustrum axon silencing on dPFC responses in both naive and trained animals. The same methodology was used as in the activation experiment, except we injected eOPN3 to silence axonal neural activity (Fig. 5A–D). Notably, eOPN3 is a light-sensitive opsin that functions as an inhibitory tool by reducing neuronal activity upon photostimulation. This contrasts with ChrimsonR, which we use for activation experiments, as ChrimsonR is a red-light-sensitive opsin that depolarizes neurons to induce excitation. Thus, while Chrimson activates neural circuits via depolarization, eOPN3 effectively silences them through hyperpolarization (activates a signaling cascade leading to hyperpolarization and subsequent silencing of neural axonal activity, providing an effective means of optogenetic inhibition42, see methods for more details).

Initially, we assessed the percentage of responsive neurons (Fig. 5E). The distribution of responsive neurons closely mirrored that observed during claustrum axon activation: 53% of the cells were responsive, with 20% classified as sensory-responsive, 30% as opto-responsive, and 11% as opto-boosted. Notably, in the opto-boosted subpopulation of naive animals (addition to opto responsive cells, p=0.031), inhibited responses were more pronounced than excited responses, a difference not seen during claustrum axon activation (Fig. 5F, green plot for opto-boosted, p=0.031). Moreover, the degree of the modulation effect was strongly correlated with the cell responsiveness (Fig. 5F, r=-0.999, p<0.001)

Then, we explored each subpopulation dynamics and found similar dynamics to the activation experiment (Fig. 5G, absolute magnitude, sensory: p=0.966; opto p=0.066; opto-boosted p=0.765; Fig. 5H ΔCV, sensory: p = 0.106; opto p = 0.776; opto-boosted p = 0.179; Fig. 5I, ΔDTW, sensory: p=0.701; opto p=0.456; opto-boosted p=0.244). The only difference was a higher change in the cross-correlation for the silencing group than the activation group Fig. 5J, A two-way ANOVA, main effects of training: F(1,34)=89.521, p<0.001, responsiveness: (F(1,34)=264.120, p<0.001, interaction F(1,34)=87.529, p = 0.002), suggesting that silencing the claustrum enhances network synchrony in the dPFC to a greater degree than activation, though through similar mechanisms.

We then the same mice on the same Pavlovian task and showed no differences between naive and trained animals in terms of the measured neural dynamics (Supplementary Fig. 7). These findings suggest that the claustrum exerts a dual regulatory influence on the dPFC, modulating its output in a flexible manner depending on whether the claustrum is activated or silenced.

Overall, these findings suggest that the claustrum serves as a critical modulator of neural activity in the dPFC, influencing both the amplitude and variability of neuronal responses as well as the overall synchrony of the neural network. The effects observed during claustral silencing versus activation provide insights into the dual regulatory capacity of this brain region, highlighting its importance in enhancing neural flexibility to optimize brain functions.

Methods
Animals
Male and female C57BL/6J or Nkx2.1Cre;Ai9 background mice were used in these experiments. Twenty four GCaMP6s transgenic mice (CamKIIa-tTa x B6;DBA-Tg(tetO-GCaMP6s)2Niell/J) were used for imaging of the claustrum axon activation and inhibition experiments. Additionally, five Nkx2.1-Cre x tdTomato were used for inhibitory neuron imaging experiments. Mice were between 5–11 weeks of age when surgery was performed. Animal experimentation was carried out in accordance with the guidelines and regulations of the UK Home Office (Animals in Scientific Procedures Act of 1986) and the University of Oxford Animal Welfare and Ethical Review Board.

Stereotaxic surgery
Mice were prepared for imaging experiments through a single surgical session that included head plate implantation, cranial window placement, and viral injection. Initially, the mice were anesthetized with 3% isoflurane, placed in a heated stereotaxic frame, and given intraperitoneal injections of 5mg/kg meloxicam (Metacam) and 0.1mg/kg buprenorphine (Vetergesic). They were maintained under 1.5% isoflurane and kept warm on a 37°C heating pad throughout the surgery. The scalp was sanitized with chlorhexidine gluconate and isopropyl alcohol (ChloraPrep), followed by the application of a local anesthetic (Bupivacaine) under the scalp. A midline incision was made in the scalp, which was then retracted to reveal the skull, leveled manually between the bregma and lambda landmarks. The site for the 4mm cranial window was marked stereotaxically (AP: -1mm to 3mm; ML: either -1mm to 3mm or -3mm to 1mm from Bregma, depending on the side of injection). A circular craniotomy was drilled at this marked location, and the skull piece was removed after saline application. The dura mater on the injected side was carefully excised with regular saline washing.

To specifically label claustrum axons in the PFC, AAV.hSyn.-Cre.WPRE.hGH (Retro-cre, 0.70e+13 gc/mL, 90nL, Addgene #105553) was injected into the retrosplenial cortex (AP: -3.0, ML: 0.5, DV: -1.0) of all mice to label the retrosplenial cortex projecting claustrum cells. Then, we injected AAV5-Syn-FLEX-rc[ChrimsonR-tdTomato] (Flex-ChrimsonR, 1.20e+13 gc/mL, 250 nL, Addgene #62723) was injected into the claustrum (AP: 1.0, ML: 3.4, DV: -2.7) for activation experiments and pAAV-hSyn1-SIO-eOPN3-mScarlet-WPRE (FLEX-eOPN3, 1.20e+13 gc/mL, 250 nL, Addgene #125713) for axon silencing experiments. Injections were performed with a glass needle and automated nanoinjector (Nanoject II™ Drummond) at a rate of 100 nL/min. The needle remained in place for 10 min post-injection to promote viral diffusion.

A cranial window, consisting of a 4mm coverslip attached to a 5mm coverslip, was then inserted into the craniotomy and sealed with cyanoacrylate (VetBond) and dental cement. An aluminium head plate with an imaging well centred on the window was then secured in place with dental cement (Super-Bond C&B, Sun-Medical). Post-surgery, mice were removed to a fresh cage, provided with meloxicam jelly for pain relief, and allowed a three-week recovery period to achieve optimal viral expression before beginning experiments.

To selectively express opsins in claustrum neurons, we injected AAV-retro-Cre into the RSP, which projects specifically to the claustrum but not to adjacent regions such as the insula. This strategy allowed retrograde delivery of Cre recombinase to a well-defined claustrum population. Subsequently, we injected Cre-dependent opsins (ChrimsonR or eOPN3) directly into the claustrum to restrict expression to claustrum neurons that received retrograde Cre. This approach minimized off-target labeling of nearby non-claustral regions and ensured selective manipulation of claustrum-originating axons within PFC. The same targeting strategy has been extensively characterized in our previous work16 and in other labs2,3, where retrograde Cre delivery from RSP combined with Cre-dependent opsin expression in the claustrum reliably labeled a large population of glutamatergic claustrum projection neurons. This population includes neurons projecting both to RSP and medial prefrontal cortex, reflecting the known collateralization of claustrum projections.

Awake head-fixed two-photon calcium imaging and widefield photostimulation
All two-photon imaging was performed using a two-photon microscope (Ultima 2p Plus, Bruker Corporation) controlled by PrairieView software (Bruker Corporation), and a femtosecond-pulsed, dispersion-corrected laser (Chameleon, Coherent). Total power was modulated with a Pockels cell (Conoptics) and maintained at or below 50mW on a sample for all experiments. Imaging was performed using a Nikon 16× 0.8NA water immersion lens. The lens was insulated from external light using a custom 3D-printed cone connected to a flexible rubber sleeve. A wavelength of 920 nm and 50mW power on the sample was used for visualizing GCaMP6s. An imaging rate of 30 Hz and a 512×512 pixel square field of view (FOV) were used for all recordings.

Visual stimulation. Visual stimuli consisted of 250ms light pulses delivered using a high-brightness 590nm LED (LYCP7P-JRJT-1-0, Farnell, UK), controlled via a National Instruments data acquisition card and PackIO software, with custom MATLAB scripts generating the timing signals. The LED was positioned ~10cm lateral and slightly anterior to the contralateral eye, at an angle of ~30–45 degrees relative to the midline to ensure direct illumination of the visual field corresponding to the recorded hemisphere. The LED was driven at 1A (manufacturer-rated luminous flux: 56 lumens). No additional focusing optics were used.

Optogenetic stimulation. Widefield (1P) optogenetic stimulation was performed concurrently with two-photon imaging using the widefield excitation module of the Bruker 2p Plus system. Orange light photostimulation (595nm, 10 pulses of 25ms at 40Hz; total duration 250ms) was delivered during 2P imaging at 1.5mW power at the sample. For optogenetic activation, we used ChrimsonR, a red-shifted opsin effective for activation (or eOPN3 for inhibition) with 594–595nm illumination36. Light power at the fiber tip was maintained at ~5–10mW/mm², sufficient to reliably activate claustrum neurons across the targeted region. This stimulation protocol was validated in the previous work using the same targeting strategy, where robust activation of claustrum projections and downstream cortical modulation were demonstrated16. To prevent photomultiplier tube damage during photostimulation, high-speed shutters were triggered to close all PMTs during optogenetic activation. Triggers for both light stimulation (Thorlabs M595L3 LED with LEDD1B driver) and PMT shutters were controlled via PackIO software.

To confirm that our two-photon imaging at 920nm did not unintentionally activate Chrimson, we relied on the original spectral characterization of Chrimson36, which shows negligible activation at 920nm. This is further supported by Packer et al.55, who found no cross-activation of the more sensitive opsin C1V1 during 920nm two-photon imaging. These recordings were conducted using a conservative imaging protocol (2–3 imaging windows and ≥40s inter-trial intervals), further reducing any possibility of opsin desensitization or tonic activation. Our primary dataset showed no systematic baseline shifts or activity drifts that would suggest 920nm–induced Chrimson activation.

Following recovery from surgery, mice were first acclimated to head fixation under the two-photon microscope. ChrimsonR and eOPN3 expression in claustrum axons projecting to PFC was then assessed visually through the cranial window under the microscope. Animals in which no labeled axons could be detected were excluded from further experiments. Only animals with clear ChrimsonR- or eOPN3-expressing axons visible in PFC were included for optogenetic activation or inactivation experiments.

During each experiment, mice were first head-fixed under the microscope. Imaging was performed in an enclosed hood to minimize visual stimuli, and white noise was used to obscure extraneous sounds. The surface of the cranial window was levelled relative to the imaging plane using a tip-tilt stage (Thorlabs). During each imaging session, mice were presented with 30 randomly interleaved stimulus presentations with and without optostimulation separated by randomly generated 8–15 sec intertrial intervals (30 visual-only trials, 30 opto-only trials and 30 visual+opto trials). These 90 stimuli were randomly drawn. The order of each trial type was randomly generated each day. After all stimuli were delivered, a new FOV was then selected and the sensory stimulation and photostimulation were repeated.

Behavioural training
Pavlovian conditioning was employed to associate a simple visual stimulus with a water reward, thereby familiarizing the animals with the visual cue prior to imaging. This approach was chosen to ensure that the visual stimuli had behavioral relevance without requiring complex training or active task performance during imaging. Associative learning paradigms like Pavlovian conditioning have been shown to robustly engage sensory, prefrontal, and subcortical circuits, including the claustrum and prefrontal cortex56, even under passive viewing conditions. By using a passive associative learning paradigm, we aimed to enhance stimulus salience while minimizing variability related to task performance, motor responses, or differences in learning strategies41.

Mice were presented with a 2-sec LED stimulus, delivered unilaterally to ensure consistent sensory exposure across animals. In 80% of trials, the LED presentation was followed by a water reward, while 20% of trials omitted reward to maintain engagement and stimulus salience. Animals underwent daily training sessions consisting of 150 trials per session, with inter-trial intervals (ITIs) randomly drawn from a uniform distribution between 8 and 15sec. Training was conducted over 7–10 consecutive days until animals reliably associated the visual stimulus with reward delivery. No active behavioral responses were required for reward delivery. Imaging sessions were subsequently performed in a passive viewing context, using the same visual stimulus but without reward delivery, allowing assessment of neural responses in a controlled, behaviorally relevant but motor-free condition.

To evaluate whether trial-by-trial fluctuations in arousal contributed to the observed neural responses, we included pupil radius—a well-established proxy for arousal—as a regressor in a generalized linear model (GLM) for each cell’s response. The model included predictors for stimulus condition (visual, opto, visual+opto), pupil radius (z-scored within session), and trial number to account for slow drift. Across the population (n=12,487 cells from 16 mice), pupil radius did not significantly explain additional variance in ΔF/F responses beyond stimulus condition (mean additional variance explained = 0.27% ± 0.06% SEM). Only a small minority of cells (3.4%) showed significant modulation by pupil radius at p<0.05 (False discovery rate corrected), and these were not spatially clustered or functionally distinct. Moreover, the presence or magnitude of optogenetic modulation (quantified by ΔF/F difference between visual and visual+opto conditions) was not systematically related to pupil size (Spearman’s ρ=0.03, p=0.21). These results suggest that arousal fluctuations were not a primary driver of the stimulus-evoked or optogenetically modulated responses reported here.

Perfusion and tissue sectioning
The presence of claustrum labeling in each animal was confirmed using post-mortem coronal slices, spanning from 1.5 mm anterior to bregma to 0 mm, as shown in Supplementary Fig. 1.1. Mice were deeply anesthetized with 5% isoflurane followed by an overdose of pentobarbital administered intraperitoneally. Subsequently, transcardial perfusion was performed using 0.01M PBS, followed by fixation with 4% PFA. The brains were extracted and allowed to fix overnight in 4% PFA. After fixation, the brains were transferred to 0.01M PBS and prepared for sectioning with a Leica VT1000S vibratome. Coronal slices, 100 μm in thickness, were obtained and either immediately mounted in 0.01M PBS or stored in a tissue freezing solution (45% 0.01M PBS, 30% ethylene glycol, 25% glycerol) at -20°C for up to three years. Before mounting, the tissue was washed three times for 5 min each in 0.01M PBS and then coverslipped. Whole-slice and claustrum images were captured at 16X magnification using a 2P microscope equipped with a Coherent Vision-S laser, Bruker 2P Plus microscope, and a Nikon 16× 0.8NA objective.

Data Analysis
All analyses were performed with custom routines using Python 3.7.9 and open source packages unless otherwise stated.

2-photon calcium imaging analysis. Calcium imaging data were preprocessed using Suite2P57 to remove motion artifacts and cell segmentation. We computed ΔF/F for each cell using the equation:

ΔF=F = (F − F)=F

where F represents the mean of F across time through the entire session. For cell ROIs selected by suite2P, F was first corrected for neuropil fluorescence by subtracting 0.7*FNeu. After calcium traces were exported from Suite2P, all analyses were carried out using custom Python and Matlab code.

Extracted calcium signals were then analyzed to identify cells that significantly responded to visual, opto or visual + opto stimulation. Significantly responsive cells were identified by using a non-parametric Mann-Whitney U test to compare the signal in the 1 sec before and after stimulus onset. Multiple comparisons correction was performed using the Benjamini-Hochberg false discovery rate analysis with an alpha of 1%.

Event time extractions. Synchronised timeseries data signals collected as individual channels in PackIO (visual cues and photostimulation) were processed with custom code. Widefield photostimulation timings were retrieved from the high-speed shutter loopback signal collected as a temporally synchronised PackIO channel. The photostimulation trigger timestamp and the total duration of the photostimulation protocol were used to define each photostimulation trial’s onset and offset. The photostimulation onset/offset timestamps were cross-referenced to the imaging frame clock signal to define photostimulation onset and offset in the imaging data. We excluded imaging frames between onset and offset of each photostimulation trial as photostimulation laser induced imaging artefacts and aberrant neuronal activity.

To quantify the intricacies of neuronal activity, we used four different metrics, absolute magnitude, coefficient of variation (CV), dynamic time warping (DTW), and empirical cumulative distribution function (ECDF). The absolute magnitude is calculated to determine the intensity of response changes by comparing mean fluorescence levels before and after a stimulus. This measure, by accounting for noise through standard deviation, aims to quantify signal changes from background fluctuations. To quantify trial-to-trial variability of neural responses, we computed the coefficient of variation (CV) based on trial-averaged response amplitudes. For each trial, we extracted fluorescence signals (ΔF/F) for a 1-sec window aligned to stimulus offset and calculated the mean fluorescence across all frames within this window, resulting in a single response amplitude per trial. We then computed the CV for each cell as the standard deviation of these trial-averaged responses divided by their mean across trials. This approach allowed us to capture variability in the overall response magnitude across repeated presentations while minimizing the influence of frame-to-frame fluctuations inherent to calcium imaging signals.

To assess trial-to-trial variability in the temporal dynamics of neural responses, we employed dynamic time warping (DTW), a well-established method for aligning time series with temporal shifts or distortions58–60. For each cell, fluorescence signals (ΔF/F) were extracted for a 1-sec window aligned to stimulus onset across all trials. DTW was then used to compute pairwise distances between the temporal response trajectories of all trial pairs for each cell. The DTW algorithm allows flexible nonlinear alignment of time series, accounting for small variations in response onset, peak latency, or shape across trials. For each cell, we quantified temporal variability as the average DTW distance across all trial pairs. This approach captures differences in temporal structure of calcium responses that may not be reflected by amplitude-based measures alone, providing a complementary index of response consistency across trials.

Finally, The ECDF further by illustrating the cumulative distribution of fluorescence data, allows us to visualize and interpret the variability and distribution of responses across different conditions. In this study, we leveraged our extensive dataset (>40,000 cells across animals) to examine population-level shifts in ΔF/F responses between experimental conditions. For each animal, we computed the mean ΔF/F response of individual cells within a fixed window following stimulus onset, and then compiled these values into condition-specific distributions.

To emphasize the structure of the bulk population and minimize the influence of outliers—which are common in large-scale imaging datasets—we focused our ECDF analysis on the central 90% of the distribution, defined between the 5th and 95th percentiles. This approach offers a more stable and representative view of population-level dynamics, preserving meaningful variability while reducing distortion from rare extreme values. By using this robust, non-parametric method, we were able to detect condition-dependent distributional shifts that reflect how widespread neuronal populations are modulated, beyond mean or median response summaries.

Together, these calculations enable a comprehensive analysis of the data, highlighting both the magnitude and variability of neuronal responses as well as their temporal coordination.

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "The manuscript by Atilgan et al. investigates the role of the claustrum in modulating neural activity in the dorsal prefrontal cortex (dPFC) of mice. Using single photon optogenetics and two-photon calcium imaging, the authors examined how claustrum axon stimulation affects dPFC neuron responses to visual stimuli. They identified three types of dPFC neurons based on their response patterns: sensory responsive, optogenetically responsive, and opto-boosted cells. The findings are: 1) Claustrum axon stimulation causes various responses in dPFC neurons. 2) At network level, Claustrum axon stimulation enhanced network homogeneity by reducing differences between average cell responses. 3) While dPFC neurons still exhibit diverse responses to visual and opto stimulation after Pavlovian training, training led to greater network homogeneity. 4) Silencing claustrum axons with eOPN3 enhances networks synchrony.\n\nWhile the study provides some data regarding how the claustrum might regulate prefrontal cortex function, the experiments failed to bring any insight into the functional understanding of this regulation. The experimental design could be improved, and there are several technical issues that need to be addressed. Overall, the study may benefit from further refinement before it is ready for publication.\n\nMajor Concerns,\n1) It is unclear why visual stimulation is chosen for stimulating dPFC neurons. The dPFC is well known for its cognitive control and executive functions. Using a cognitive task would be a more appropriate method to study its function and regulation.\n2) To study the influence of claustrum to dPFC neural activity, the authors stimulate the claustrum simultaneously with the visual stimuli. This approach might introduce several issues. First, it is unknown whether claustrum cells are activated or inhibited by the visual stimuli, making it uncertain whether the optogenetic stimulation is physiologically relevant. Second, the timing of optogenetic stimulation needs to be systematically addressed. The authors should conduct a detailed analysis of the effects of adding optogenetic stimulation at different time points to understand both feedforward and feedback regulation.\n3) According to the methods section, the authors labeled PFC-projecting claustrum axons by injecting AAV-retro-Cre in the retrosplenical cortex and Cre-dependent Chrimson/eOPN3 in the claustrum. The authors need to clearly explain why this method can label PFC-projecting axons from claustrum. Shouldn’t the AAV-retro-Cre be injected into the PFC instead?\n4) It is unclear at all which types of claustrum neurons or extent of their labeling are achieved using their method. A systematic profiling of the cell types of Chrimson/eOPN3 expressing neurons and a quantitative estimate of the infected cells are required for a clear evaluation of the results. The diverse responses of dPFC neurons observed may be simply due to incomplete and biased labeling of different types of claustrum neurons.\n5) It remains uncertain whether the 1p optogenetic stimulation can effectively recruit the firing of claustrum neurons. Factors such as insufficient light power, low expression of Chrimson, and feedforward inhibition by visual stimuli can easily jeopardize the intended manipulations of the claustrum.\n\nMinor,\n1) I am not sure why the authors classify dPFC neurons as sensory, opto, O-boosted. Shouldn’t there be a category for neurons that are both sensory and optogenetically responsive?\n2) In fig.1 E, F the authors should show responses of these neurons to Optogenetic stimulation.\n3) I am not sure whether Fig.1 I is serving any purpose here.\n4) Supplementary fig.1.1 is hard to read. The use of color could improve clarity.\n5) Fig. 5K is missing (line 319)\n6) Line 132 is misleading. The E:I should be better defined.\n7) Writing needs improvement, and much more detail is required in the methods section for readers to follow.", "reviewer_2": "The authors show intriguing complexity in claustrum-PFC interactions by presenting visual stimuli, photostimulating claustrum axons, or both while using calcium imaging to measure the activity of many individual neurons in dPFC. PFC responses were diverse, including visual-responsive, opto-responsive, and neurons that only responded in the presence of both types of stimuli. Opto-responsive cells were more likely to be inhibited than excited, which is consistent with previous studies. Overall, the responses to visual and opto stimuli were negatively correlated such that a positive response to visual stimuli predicts a negative response to opto stimuli and vice-versa.\n\nIn general, visual stimuli plus opto stimulation of claustrum axons increased trial-trial variability of individual neuron responses compared to visual stimuli alone. In contrast, cross-correlation between individual neurons’ responses to visual stimuli across trials was enhanced by claustrum axon stimulation, showing increased network homogeneity.\n\nThe authors then repeated these experiments in a subset of animals that had been trained to associate the visual stimulus with reward and found the major difference between trained and naïve animals was a further increase in network homogeneity due to claustrum axon activation as measured by average cross-correlation.\n\nThe authors then expressed an inhibitory opsin in claustrum axons, repeated experiments, and unexpectedly found a larger increase in network homogeneity due to claustrum axon suppression versus activation. However, training did not further increase network correlation due to claustrum axon suppression as it did with excitation. This may suggest a different network mechanism responsible for effects due to suppression vs. excitation.\n\nThe work is significant and complements the established literature, elaborating on how claustrum input affects responses of PFC neurons. In particular, the description of the interaction of effects between claustrum input and sensory input, and how training specifically enhances the network synchrony effect, is very interesting and to my knowledge, represents a novel result in the field.\n\nThe result that claustrum axon activation and suppression have very similar effects is still puzzling to me, but understanding mechanisms behind that phenomenon would be worthy of an entire future research project. The manuscript as it stands is an impressive amount of work and is worthy of acceptance with some revisions. No more experiments are necessary, although more method details should be added (see notes below).\n\nThe methodology is sound (though I don’t have experience to judge the specifics of calcium imaging techniques), and the experimental work is impressive. I have a handful of specific methods questions:\n1. Could you provide more details about the visual and optogenetic stimuli, ideally in the main text or figure legend? i.e. duration of stimulus, positioning of visual stimulus relative to the head/eyes of the animal, wavelength & intensity of the visual stimulus.\n2. What type of random distribution was used for drawing inter-trial intervals? Uniform, or exponential, or other?\n3. On line 124 you wrote: …”displayed responsiveness to visual stimuli only when preceded by photostimulation…” – this implies the optogenetic stimulation came on before visual stimulation – is this a typo or was there a time difference in when opto stimuli and visual stimuli were presented?\n\nI also have some specific data analysis questions:\n1. For comparisons in Figure 1, responsiveness is “defined based on an independent t-test conducted 1 second before and after either visual, opto, or visual+opto stimuli trials”. Is this after the onset of stimuli (time windows used are [-1,0] and [0,1]), or after the end of the stimuli (time windows [-1,0] and [0,1]+stimulus_duration). Please clarify in the text and methods.\n2. E:I ratios are listed in integer form (i.e. in line 135) – it would be easier to understand if a decimal was listed as well in parentheses (“33:67 (0.49)” or “28:72 (0.39)”). Also, please add the ratios for sensory-responsive and opto-boosted cells even though they were not significant, it’s useful for comparison.\n3. For the empirical cumulative distribution function analysis (supplemental figure 2.1), are these just cumulative distribution plots of the same data from figures 1E, I, and M? If so, why do they start at around 0.5 instead of 0?\n4. Coefficient of variation: how did you compute this? Average response over some some window in each trial, then compute variability across trials?\n5. Dynamic time warping: why was this technique used? Is it because the imaging sampling rate doesn’t always line up perfectly with the stimulus onset?\n6. What exactly is being compared with DTW? Is it the average similarity between every pair of trials in a given condition?\n\nFinally, some interpretations and conclusions would benefit from additional citations or explanation.\n1. Discussion, line 354-358: is there an example of such compensatory synchronization in the literature? If the network can compensate and cause even more synchronization than claustrum input does, then why is claustrum input required in the first place? Also, higher synchronization tends to be associated with reduced information processing capacity, i.e. sleep or disengaged states.\n2. Line 375 “a distinction not observed during activation” – should this instead be “during suppression” ? Otherwise the sentence is confusing to me. If it is “during activation” could you clarify?\n3. Line 390: you discuss different response kinetics between eOPN3 and ChrimsonR, however you do not describe what those differences are and how you would expect them to potentially affect results. Please provide these descriptions along with citations in this paragraph.\n\nNote: the reference to the code in the manuscript has a typo: huriteatg/clapfcstimulation\nThe analysis code looks reasonable and does include a README file with instructions. However the data does not appear to be shared, or if it is, it's not obvious where to download it. Since the data is unavailable I was unable to re-run the figure plotting code.\nI'm also curious why MATLAB is listed as a requirement, but python is not, since almost all of the code in the repo appears to be in python, and in the manuscript python is listed as the main programming language. The few MATLAB files appear to be for generating stimuli and/or running stimuli for the experiments themselves, which is great to share, but the version of python and other packages used for analysis should be shared as well.\n\nPlease update the README to include:\n1) how to download the dataset (could just be the processed data)\n2) instructions for installing python and other required packages that were used to perform the analysis. This could come in the form of an environment.yml or requirements.txt file for ease of installation.", "reviewer_3": "Here the authors use 2p calcium imaging of the dPFC while performing visual stimulation, optogenetic stimulation of claustrum axons (in the dPFC) and a combination of visual + opto. They first classify the proportion of neurons are modulated by different stimulation combinations. Then they show that when mice are trained on a Pavlovian task, the stimulation leads to what they refer to as greater network homogeneity. The experimental approach is technically innovative. However, this paper has considerable shortcomings.\n\nMajor concerns.\n1) I do not see control experiments presented in the manuscript. Performing widefield red-light optogenetics with 2p imaging in an otherwise dark chamber, will generate at least some visual-related responses, as it is very difficult to effectively shield the animal’s visual system from this unintended visual stimulation arising with the 1P light. I recognize that the authors are experts in optogenetics + 2P imaging (although previously holographic methods were mainly used). Nonetheless, the interpretation of these results hinges on how much these distributions are different from that expected by chance, or proper light controls. All figures should contain this control group, and comparisons made between control versus opto. Control experiments are needed here, minimally to evaluate and compare effect sizes between groups and to ensure that the delivery of light does not impact visual cortex. The authors could do 2p imaging in visual cortex in mice with no opsin to determine if there are any unintended visual evoked responses with the widefield 1p stim.\n2) GCAMP6s was used which is a very slow sensor, which will underestimate true spiking levels and provide insufficient temporal resolution to truly measure the response to claustrum stimulation (and likely visual stimulation as well). For example, many PFC neurons will be inhibited by claustrum stimulation, but this inhibition arises fast, and lasts ~ 150ms. Many of these neurons will undergo rebound excitation at ~ 200ms. These experiments used a 250ms opto period, where gcamp imaging is masked. Therefore, this masking period misses most of the interesting dynamics that would accompany optogenetic activation of claustrum axons. A cell that fired rebound excitation would be classified as being activated, because the suppression period would not be detectable. With the number of caveats that arise with 2p imaging here, a much better approach to perform these experiments would be to use extracellular electrophysiology. Minimally the authors should repeat their main experiments (figure 1) using electrophysiology where effective temporal resolution and single spiking can be more accurately measured.\n3) A related note of concern with respect to the experimental design is the use of only 30 trials. This is a very small number of trials, and therefore, certain types of responses are more likely to be observed. For example, the detection of inhibitory responses requires some degree of baseline spontaneous activity. Therefore, cells with low spontaneous activity rates are likely to be classified as nonresponsive or activated. The detection of inhibition in low activity cells requires many more trials. Given the trial-to-trial variability, even in the visual system, 30 trials seem far too low.\n4) Behavioral state is not mentioned or controlled for in this manuscript. PFC neurons and sensory encoding in the visual cortex are robustly modulated by small twitches of the face, or changes in arousal. Sorting the opto trials based on arousal levels would help add some insight and it would be important to control for this variable.\n5) Claustrum fibers are most dense in deep cortical layers, but this widefield 1p stimulation will mainly activate superficial cortical layers. Even if 1.5mW of 590nm light can reach the deep layers there will be an asymmetric activation of axons in layer 1-2 and therefore, it is unclear how well this stimulation models what actually happens when claustrum neurons are activated. Experiments where claustrum cell bodies are stimulated would address this concern.\n6) Can the authors confirm that the 50mW 920nm light is not activating the opsin in vivo? This would be important to understand to what extent (if any) axons were being tonically depolarized by 2p imaging light. Performing in vivo field potentials under the microscope objective, while turning on and off the scanner would provide insight into if the axons are being activated by 920nm light. If axons are being activated by the 920nm scanning, then field potential deflections will be present at short latency after the scanning is turned on.\n7) Presenting and quantifying the data using “cells” rather than mice or fields of view, massively increases type 1 error rates. Doing a t-test with an n=1000 is and obtaining a p-value of 0.03 is not convincing. In some cases, the authors report mice rather than cells. Is there a reason for not reporting the n’s as mice in all figure panels?\n8) Related to control experiments mentioned above – the authors should show that the widefield axon activation (Chrimson) or suppression (OPN3) increases or decreases the output of claustrum axons (respectively), under experimentally relevant conditions. Electrophysiological experiments help accomplish this.\n9) I am confused by the Pavlovian task. What is the rationale for doing this task here? Is there some relevance to claustrum or PFC? No mention or discussion of this was provided. Also, it seems that imaging in trained animals occurred after training, so these mice had been trained to response to visual inputs, but then during imaging, they did not perform the task. This aspect of the paper was confusing.\n10) Can the authors show that this claustrum-PFC effect is specific? For example, does activating the dorsomedial thalamus also generate response variability? It seems that any long-range glutamatergic input to the PFC would result in an interference of incoming visual information.\n11) Consider double checking that the correlation in Figure 5F is indeed r = -0.999. This value seems biologically improbable given the spread of points.\n12) In figure 3G, the cross-correlation matrix shows ~ 20k cells. This implies that the 20k cells were simultaneously imaged. Which was not the case as this is the number of cells across all fields of view. Therefore, I’m not sure how they can measure the pairwise correlation between cells that were not imaged simultaneously.\n13) The pie plots throughout are ineffective, confusing, and not proportional (Figure 1I). They detract from the paper.\n14) The authors state that 14% of the cells (n = 5783), were classified as opto-boosted cells, and displayed responsiveness to visual stimuli only when preceded by photostimulation. I am confused by the term “preceded”. Did they offset the opto stim from visual stimulation? There was no indication of this, and so when I read the paper I am confused.\n15) What is the dynamic time warping analysis? This is central to the paper, yet the authors do not describe mathematically what this measure is or how it is calculated. No reference is given. Therefore, I cannot (and I did not) review anything related to this measure when I read the paper."}, "author_response": "We thank the reviewer for this important point. Indeed, the dPFC is classically associated with cognitive control, decision-making, and executive functions. Our decision to use visual stimulation as the sensory input was driven by several scientific and practical considerations, which we clarify below:\n1) Evidence of visual representations in dPFC: Although dPFC is primarily known for higher-order functions, accumulating evidence suggests that a subset of dPFC neurons exhibits sensory responsiveness, including to visual stimuli. For example, Wool et al. (2023) and Moore & Armstrong (2003) have shown that approximately ~20% of dPFC neurons can respond to visual inputs, particularly when those inputs are behaviorally salient or novel. Our results are consistent with these prior studies, as we identified ~21% of neurons classified as sensory responsive to visual cues in naive animals (see Fig. 1J, 10,531/49,212 cells). Thus, the visual modality offers a tractable and well-characterized stimulus space to probe dPFC activity.\n2) Controlling circuit complexity: A major aim of our study was to isolate and characterize the modulatory influence of claustrum input on dPFC neural responsiveness. The dPFC is highly interconnected with multiple brain regions involved in motor, attentional, and motivational processes. Introducing complex cognitive tasks would likely recruit multiple parallel systems, potentially confounding the direct interaction we sought to study between claustrum axons and dPFC neurons as these regions also have also connection to the claustrum. By using a simple passive visual stimulus paradigm, we minimized engagement of other circuits while still being able to evoke reproducible neural responses.\n3) Minimizing experience-dependent plasticity and inter-individual variability: Cognitive tasks often require extensive training, which introduces additional variability across animals due to differences in learning speed, strategy, or motivation. Our approach allowed us to study dPFC responses both in naive and trained states (after Pavlovian conditioning), enabling us to directly compare baseline and learned states. Indeed, our findings showed that the claustrum’s modulation of variability and homogeneity was robust across both naive and trained animals (see Figs. 2, 3, 4, 5). Using a simple stimulus minimized confounds related to learning-induced plasticity that may have obscured these mechanistic insights.\n4) Consistency with claustrum literature: The claustrum itself has been extensively implicated in salience processing and sensory integration (Goll et al., 2015; Chevée et al., 2022; Atlan et al., 2024). Using a sensory paradigm aligned with prior literature allowed us to dissect the sensory gating functions of the claustrum onto prefrontal circuits directly.\nIn future studies, we fully agree that adding more complex cognitive paradigms would be an important next step to test how claustrum modulation generalizes to higher cognitive processes in the dPFC. However, for this initial mechanistic dissection, a simple visual paradigm provided a controlled platform to uncover the bidirectional and cell-type-specific effects of claustrum input on dPFC neural dynamics.\nWe have now added clarifications of these points in the revised manuscript in Introduction section Line 78 - 94;\n‘... In this study, we address this by selectively stimulating claustrum axons within the PFC, enabling us to probe the functional consequences of direct claustro-PFC input while minimizing polysynaptic recruitment of other brain regions. This approach allows us to directly investigate how claustral input modulates prefrontal activity. Supporting this strategy, recent anatomical and optogenetic evidence has demonstrated that a subset of claustrum axons projecting to the PFC exhibit robust light-evoked responses (Shelton et al., 2024), providing a foundation for targeted, circuit-specific manipulation.\nBuilding upon this foundation, our study aims to elucidate the claustrum’s contribution to PFC neural responsiveness, exploring its influence in both naive and trained states. To probe the functional consequences of this pathway, we combined local claustrum axon photostimulation embedded within the PFC with passive visual stimulation while recording PFC population activity using two-photon calcium imaging. We leveraged the known sensory responsiveness of dPFC neurons (Wool et al., 2023; Moore & Armstrong, 2003) and used passive visual stimuli to avoid confounding effects of task demands, motivation, or motor output—factors known to interact with both claustrum and PFC circuits (Madden et al., 2022). This approach enabled a controlled examination of how claustral input shapes local PFC computation.’\n\n[... (truncated for brevity, full response follows the same structure)]"}, {"round": 2, "reviewer_comments": {"reviewer_1": "The authors have addressed most of the concerns. I have no further comments.", "reviewer_2": "I thank the authors for thoroughly addressing the reviewer comments, and I recommend publication."}, "author_response": ""}]

Decision letter
REVIEWER COMMENTS ( 25 April, 3 months)
Reviewer #1 (Remarks to the Author):
The manuscript by Atilgan et al. investigates the role of the claustrum in modulating neural activity in the dorsal prefrontal cortex (dPFC) of mice. Using single photon optogenetics and two-photon calcium imaging, the authors examined how claustrum axon stimulation affects dPFC neuron responses to visual stimuli. They identified three types of dPFC neurons based on their response patterns: sensory responsive, optogenetically responsive, and opto-boosted cells. The findings are: 1) Claustrum axon stimulation causes various responses in dPFC neurons. 2) At network level, Claustrum axon stimulation enhanced network homogeneity by reducing differences between average cell responses. 3) While dPFC neurons still exhibit diverse responses to visual and opto stimulation after Pavlovian training, training led to greater network homogeneity. 4) Silencing claustrum axons with eOPN3 enhances networks synchrony.
While the study provides some data regarding how the claustrum might regulate prefrontal cortex function, the experiments failed to bring any insight into the functional understanding of this regulation. The experimental design could be improved, and there are several technical issues that need to be addressed. Overall, the study may benefit from further refinement before it is ready for publication.
Major Concerns,
1) It is unclear why visual stimulation is chosen for stimulating dPFC neurons. The dPFC is well known for its cognitive control and executive functions. Using a cognitive task would be a more appropriate method to study its function and regulation.
We thank the reviewer for this important point. Indeed, the dPFC is classically associated with cognitive control, decision-making, and executive functions. Our decision to use visual stimulation as the sensory input was driven by several scientific and practical considerations, which we clarify below:
1) Evidence of visual representations in dPFC: Although dPFC is primarily known for higher-order functions, accumulating evidence suggests that a subset of dPFC neurons exhibits sensory responsiveness, including to visual stimuli. For example, Wool et al. (2023) and Moore & Armstrong (2003) have shown that approximately ~20% of dPFC neurons can respond to visual inputs, particularly when those inputs are behaviorally salient or novel. Our results are consistent with these prior studies, as we identified ~21% of neurons classified as sensory responsive to visual cues in naive animals (see Fig. 1J, 10,531/49,212 cells). Thus, the visual modality offers a tractable and well-characterized stimulus space to probe dPFC activity.
2) Controlling circuit complexity: A major aim of our study was to isolate and characterize the modulatory influence of claustrum input on dPFC neural responsiveness. The dPFC is highly interconnected with multiple brain regions involved in motor, attentional, and motivational processes. Introducing complex cognitive tasks would likely recruit multiple parallel systems, potentially confounding the direct interaction we sought to study between claustrum axons and dPFC neurons as these regions also have also connection to the claustrum. By using a simple passive visual stimulus paradigm, we minimized engagement of other circuits while still being able to evoke reproducible neural responses.
3) Minimizing experience-dependent plasticity and inter-individual variability: Cognitive tasks often require extensive training, which introduces additional variability across animals due to differences in learning speed, strategy, or motivation. Our approach allowed us to study dPFC responses both in naive and trained states (after Pavlovian conditioning), enabling us to directly compare baseline and learned states. Indeed, our findings showed that the claustrum’s modulation of variability and homogeneity was robust across both naive and trained animals (see Figs. 2, 3, 4, 5). Using a simple stimulus minimized confounds related to learning-induced plasticity that may have obscured these mechanistic insights.
4) Consistency with claustrum literature: The claustrum itself has been extensively implicated in salience processing and sensory integration (Goll et al., 2015; Chevée et al., 2022; Atlan et al., 2024). Using a sensory paradigm aligned with prior literature allowed us to dissect the sensory gating functions of the claustrum onto prefrontal circuits directly.
In future studies, we fully agree that adding more complex cognitive paradigms would be an important next step to test how claustrum modulation generalizes to higher cognitive processes in the dPFC. However, for this initial mechanistic dissection, a simple visual paradigm provided a controlled platform to uncover the bidirectional and cell-type-specific effects of claustrum input on dPFC neural dynamics.
We have now added clarifications of these points in the revised manuscript in Introduction section Line 78 - 94;
‘... In this study, we address this by selectively stimulating claustrum axons within the PFC, enabling us to probe the functional consequences of direct claustro-PFC input while minimizing polysynaptic recruitment of other brain regions. This approach allows us to directly investigate how claustral input modulates prefrontal activity. Supporting this strategy, recent anatomical and optogenetic evidence has demonstrated that a subset of claustrum axons projecting to the PFC exhibit robust light-evoked responses (Shelton et al., 2024), providing a foundation for targeted, circuit-specific manipulation.
Building upon this foundation, our study aims to elucidate the claustrum’s contribution to PFC neural responsiveness, exploring its influence in both naive and trained states. To probe the functional consequences of this pathway, we combined local claustrum axon photostimulation embedded within the PFC with passive visual stimulation while recording PFC population activity using two-photon calcium imaging. We leveraged the known sensory responsiveness of dPFC neurons (Wool et al., 2023; Moore & Armstrong, 2003) and used passive visual stimuli to avoid confounding effects of task demands, motivation, or motor output—factors known to interact with both claustrum and PFC circuits (Madden et al., 2022). This approach enabled a controlled examination of how claustral input shapes local PFC computation.’