Rapid dynamics of dorsal raphe serotonin neurons regulate the strength of visual attention

Domain: biology
Published: 2026-03-18
Editorial outcome: major_revision

Abstract
Every moment, a spotlight of attention roams our visual field to enhance detection of salient stimuli. While recent work suggests how the brain selects salient relevant stimuli (attentional focus), it is unknown if there are mechanisms that modulate attention to suit fluctuating target salience or internal needs (attentional strength). Here, using optical recordings and perturbations in mice performing a cued detection task, we report that rapid dynamics in both dorsal raphe (DR) activity and cortical serotonin (5HT) release selectively regulate attentional strength. In contrast to these rapid DR-5HT dynamics, we found previously reported reward-associated and slower dynamics were both unrelated to attention. Brief optogenetic DR-5HT suppression increased attentional strength and performance, whereas optogenetic excitation reduced attentional strength and performance. A computational model suggests DR-5HT’s inverse effects on attention arise from control of cortical suppression and divisive normalization. Collectively, our results demonstrate that the brain independently controls attentional strength and focus and defines rapid DR-5HT neuromodulation as a new regulator of attentional strength.

Introduction
Visual attention enhances detection of task-relevant visual targets1–4. Significant effort has been devoted to how the brain selects target locations and features1–10 (referred to as attentional focus). However, the same targets could be weakly or strongly attended (attentional strength). Control of attentional strength, independent of attentional focus, could let the brain tailor enhancement of stimuli to meet fluctuating target salience or internal need. However, evidence for this separation is currently unavailable. Moreover, while many studies suggest cortical mechanisms for attentional focus1–10, less is known about the mechanisms underlying attentional strength.

Neuromodulators have been proposed as candidates for control of visual attention11–14, and of these, serotonin (5HT) is particularly intriguing. Forebrain 5HT originates from projection neurons in the dorsal raphe (DR) that encode internal states such as reward15–17 or patience18–22, and track uncertainty23–26 in various behavioral paradigms. DR neurons innervate many cortical areas including visual, parietal, and frontal areas27–31; such areas are believed to hold distributed representations of attention targets1–10. Recent optogenetic studies reveal that DR-5HT modulates both baseline and visually driven responses in V1 neurons32,33 which echoes prior 5HT iontophoresis work in non-human primates34–36. Thus, 5HT reflects goal-related internal states, is released in both visual and frontal areas, and modulates visual neuron excitability. Yet, whether 5HT is involved in visual attention is unknown.

To address this question, we optically monitored and manipulated DR-5HT signals while mice performed a cued visual detection task and analyzed their responses with a behavioral model that independently measured attentional strength and focus. Simultaneous fiber photometry revealed that rapid fluctuations in DR neural activity predicted attention to cued stimuli; brief DR suppression predicted strong attention and performance whereas brief excitation predicted weak attention and performance. 5HT release dynamics in visual cortex mimicked DR dynamics and also predicted attentional strength. Rapid DR-5HT dynamics had no impact on attentional focus, nor did it affect states of arousal, impulsiveness, or engagement. Optogenetic excitation of DR neurons deteriorated attentional strength and performance whereas optogenetic suppression increased attentional strength and performance. Neither optogenetic perturbation affected attentional focus, nor did they affect other internal states. A computational model suggests DR-5HT’s inverse effects on attention arise from control of cortical suppression and divisive normalization. These results demonstrate the separability of attentional strength and focus and define DR-5HT as a new regulator of attentional strength.

Results
Behavioral measurements of attention from a noisy cued-detection task
We used a cued detection task that controls contextual signals related to attention and measures other internal states that influence perception and performance37. Briefly, water restricted mice were head-fixed atop a platform facing a pair of angled screens and trained to search for 3 white vertical bars that emerged from dynamic checkerboard noise (Fig. 1a). We varied trial difficulty by varying the amount of noise combined with the 3-bars (Fig. 1b, coherence). Trials began with a tone and static checkerboard on one screen which cued the eventual location of the 3-bar grating (Fig. 1a). Mice licked a spout to start trials (start lick), which caused the cue to fade while zero-coherence dynamic checkerboard noise was presented on both screens for a random delay period (3-12 sec) until the grating appeared (Fig. 1a, delay). A lick within the reaction time (RT) window after grating appearance (hits) led to liquid reward. Gratings switched sides after ~35 trials and had a flat probability of appearance in time. Since gratings were noisy and appeared unpredictably, our task forced mice to continually evaluate checkerboards for evidence of the 3-bars. Trained mice detected weak gratings reliably (d-prime ≥ 1, coherence = 0.3, Fig. 1c).

Licks to pure noise were false alarms (FAs). FAs occurring within the RT window after zero-coherence grating appearance were unrewarded and used to compute psychometric measures (Fig. 1c). FAs during the delay period caused trials to restart until the delay period was lick-free (Fig. 1a, restart). Most mice FA licked to noise, and delay periods were seldom without FAs. We previously showed that many FAs occur because mice react to grating-like features that emerge randomly from the noise. By reverse correlating FAs to the noisy checkerboards, we saw that mice attended to the cued side for increases in verticality and local contrast 37. Here, we simplified this prior approach to better quantify visual attention.

Briefly, we convolved checkerboards with a vertical Gabor filter to extract vertical energy and a 2D Gaussian filter to extract local contrast energy (Fig. 1d). Next, we binned both energy maps in a 3x5 grid (bin = ~11o) encompassing the entire checkerboard. Finally, a logistic regression model weighed energies on cued and uncued screens to predict all FA licks. Fitting this model to each mouse provided two sets of weights that captured: 1) behavioral gains for cued and uncued screens (αCued and αUncued); and 2) sensitivity to vertical (βV) and contrast energy (βC) across both screens. We defined an attention index computed from behavioral gains to measure attentional strength whereas attentional focus was characterized by sensitivity to verticality and contrast (maps of βV and βC).

This behavioral model had significant predictive power when fit to each mouse (Supplementary Fig. 1a). Sensitivity maps revealed that mice focused on verticality (βV) and local contrast (βC) at central screen locations (Fig. 1e, f; Supplementary Fig. 1b), consistent with our earlier study37. Moreover, our attentional index quantified that mice weighed cued stimulus energies ~60% more than uncued energies (Fig. 1g). Taken together, these results show that mice attended to bar-like features at the center of the cued screen to perform our task.

DR neural activity is linked to behavior
Next, we fiber implanted mice that expressed the genetically encoded calcium indicator (GCaMP6f) within DR-5HT neurons (Fig. 2a-f, Supplementary Fig. 1c-g). We grouped datasets from two DR-5HT Cre lines (epet1-Cre and Cdh13-CreER), because they similarly marked DR-5HT neurons27–29,31 (Supplementary Fig. 2a-c) and had similar photometry signals (Supplementary Fig. 2d-g).

DR-5HT neural activity fluctuated rapidly across trial length (Supplementary Fig. 1h) and reacted to behaviorally important lick events (Fig. 2f-g). Hits were associated with positive transients after grating presentation whereas misses were not (Fig. 2h), consistent with prior work on DR activity with reward15,17. However, we noticed that average DR-5HT activity just before grating onset (at the very end of the delay period) was lower in hit trials than in miss trials (Fig. 2h-i, arrows). This pre-grating DR-5HT signal differs from prior work on task-related DR signals in a few ways. First, it is an order of magnitude faster than previous reports which show minute-long, slow DR dynamics16. Second, we saw no trial-level correlation between this pre-grating signal and the post-grating response for all hit trials (Supplementary Fig. 2h). Third, we saw no correlation between a hit trial’s post-grating response and the next trial’s pre-grating signal (Supplementary Fig. 2i). These data indicate that rapid DR-5HT dynamics just before grating onset are separate from post-grating transients associated with rewards. These results also reveal that rapid pre-grating suppression in DR activity precedes hits, whereas pre-grating DR activity in miss trials was more elevated in comparison. Given these results we next asked if rapid DR-5HT dynamics were linked to task performance.

DR neural activity predicts behavioral performance and attention
We began by measuring task performance for trials where DR-5HT activity was low or high before grating presentation (Fig. 3a). Psychometric measures (% hits, d-prime, threshold) showed that mice detected low-coherence gratings better when DR was low as compared to when it was high (Fig. 3b-d); no changes were seen in reaction time, response bias, lapse rate, or criterion (Fig. 3e). These results show that low DR activity predicts higher sensitivity to gratings (d-prime, hits, threshold) rather than changes to impulsivity (response bias), engagement (lapse rate), or strategy (criterion). The performance difference between trials sorted by pre-grating DR-5HT high and low faded away at high coherences; this is likely why pre- and post-grating ΔF/F over all hit trials was uncorrelated (Supplementary Fig. 2h) Finally, these links to performance were uniquely associated with fast dynamics, since the same analysis for slow DR-5HT dynamics (Supplementary Fig. 3a-e) and fast-slow interactions (e.g. fast suppression on a slow rise versus on a slow fall, Supplementary Fig. 3f-h) showed no correlation with task performance.

We hypothesized that the enhanced detection of weak coherent gratings during low DR activity (Fig. 3b-d) reflected the well-known ability of attention to enhance weak stimuli1–4. If true, then our behavioral measures of attention should be correlated with DR activity. Specifically, low DR activity could improve attentional focus (maps of βV and βC), could increase attentional strength (attention index), or could improve both attentional strength and focus. To test this idea, we divided FA licks based on whether they were preceded by low or high DR activity (Fig. 3f) and then measured attentional strength and focus.

Sensitivity maps weighted by attention revealed emphasis on verticality and local contrast at the center of the cued side when the DR was low; this emphasis was qualitatively weaker when the DR was high (Fig. 3g-h). Sensitivity to vertical energy and local contrast remained at the center of the screen in both DR high and low (Fig. 3h, Supplementary Fig. 4a-b). In contrast, attentional indices from DR low were significantly stronger than those computed from DR high (Fig. 3i). These results indicate that rapid DR low dynamics are correlated with increases in attentional strength rather than changes in how mice allocated vertical/contrast sensitivity across the screen (attentional focus).

We next analyzed behavioral attention measures just after our cue changed sides. Previous studies revealed that subjects often show poorer performance at block switches in cued tasks because it takes time for them to reorient attention to the new cued location or feature38–42. To address this idea, we compared attentional strength and focus between two pairs of FAs: (1) FAs preceded by low DR-5HT occurring early in the block (after the cue switched sides) and all FAs; (2) FAs preceded by high DR-5HT occurring early in the block and all FAs. This comparison revealed weaker attention indices early in the block for both DR-5HT high and low FAs as compared to all other FAs (Supplementary Fig. 4c), consistent with prior work38,39. However, early-block attention indices were higher when DR was low as compared to when it was high (Supplementary Fig. 4c). Importantly, all these changes in DR-5HT activity and attentional strength arose without effects on sensitivity to vertical and contrast energy (Supplementary Fig. 4d-e). Thus, our results so far indicate that rapid DR-5HT dynamics selectively predict attentional strength rather than attentional focus.

Optogenetic manipulation of DR neurons changes behavioral performance and attention
The inverse correlation between attentional strength and rapid DR-5HT dynamics strongly suggested that DR-5HT regulates attention. To test this hypothesis, we selectively expressed excitatory or inhibitory optogenetic actuators in DR-5HT neurons (Fig 4a-b) and asked whether suppressing or elevating their activity could alter attention and detection behavior (Fig. 4c). Briefly, mice expressing light-activated excitatory (ChR2) or inhibitory (Jaws) opsins in DR-5HT neurons were implanted with optical fibers and fully trained on the task before optogenetic manipulations began. Three seconds of light from a LED was used to drive either Jaws or ChR2 beginning two seconds before each grating presentation (Fig. 4d). If mice FA licked during the stimulation window, the LED was stopped, the cue was shown and the trial restarted (Fig. 4d, Restart). Since FA licks could occur prior to LED onset or in the two seconds between the onset of the LED and the stimulus grating, we could compare attention indices from FAs that were directly preceded by stimulation (LED on), with FAs in optogenetic sessions that were not preceded by stimulation (LED off), as well as FAs in non-optogenetic sessions (Ctrl).

Exciting DR-5HT neurons significantly increased psychometric threshold and reduced d-prime for 3-bar gratings with no effect on reaction time (Fig. 4e-f, Supplementary Fig. 5a). These effects arose without significant change in response bias, lapse rate, and criterion (Fig. 4g, Supplementary Fig. 5c), indicating that DR excitation reduces sensitivity to gratings rather than affecting impulsiveness, engagement, or strategy. We saw weaker weighted sensitivity maps and significantly reduced attentional indices when the DR was excited as compared to both unstimulated control conditions (Fig.4h-i, LED on vs Ctrl and vs LED off; LED on population is not significantly different than zero even though the fitted values for two mice showed uncued bias). Importantly, this optogenetic reduction in attention arose without any change in lick rate (Supplementary Fig. 5d), indicating that DR-5HT excitation deteriorates attention to cued stimuli rather than changing other internal states such as arousal or engagement. Thus, we conclude that elevating DR-5HT activity selectively reduces attentional strength and deteriorates performance.

Optogenetic suppression of DR-5HT neurons had the opposite effect and significantly increased d-prime and decreased psychometric threshold with no effect on reaction time (Fig. 4j-k, Supplementary Fig. 5b). Again, these improvements arose without significant changes to reaction time, response bias, lapse rate, criterion, and overall lick rate (Fig. 4l, Supplementary Fig. 5c-d), indicating that low DR activity increased sensitivity to cued gratings rather than changing impulsivity, engagement, or strategy. We saw stronger weighted sensitivity maps and significantly increased attentional indices when the DR was suppressed as compared to both unstimulated control conditions (Fig. 4m-n, LED on vs Ctrl and vs LED off). No performance or sensitivity changes were seen when we optically stimulated a sham control mouse (Supplementary Fig. 5e-f). Moreover, spatial sensitivity to vertical and contrast energy remained centered during both kinds of optogenetic perturbation (Supplementary Fig. 5g-j). These results indicate that DR-5HT suppression selectively strengthens a mouse’s attention to cued grating-like patterns rather than impacting arousal, engagement, or attentional focus.

These optogenetic studies support our photometry observations and indicate that rapid DR actvity selectively regulates attentional strength without changing attentional focus or other internal states.

V1 serotonin release dynamics predict visual attention and behavioral performance
Since prior work reported neural correlates of attention in mouse V138–42, we next asked if the DR-5HT attention signal appears in this cortical area. For this experiment, we delivered AAVs bearing the genetically encoded 5HT sensor GRAB5HT43 into V1 and wide-field imaged fluorescent signals while mice performed our task (Fig. 5a). Tissue sections through V1 showed broad expression of GRAB5HT across cortical laminae (Fig. 5b), and GRAB5HT showed similar task-related signals as in our photometry dataset (Supplementary Fig. 6a).

Importantly, grouping trials by 5HT low and high release in the period just preceding grating appearance (after the long delay), revealed signals resembling our DR photometry data (Fig. 5c-d). Trials with low pre-grating GRAB5HT signals had significantly higher hits, higher d-prime, and lower psychometric thresholds, with no change in reaction time (Fig. 5e-f). These effects arose without significant changes to the response bias, lapse rate, and criterion, suggesting low 5HT release in V1 is associated with better stimulus detection rather than differences in impulsivity, engagement, or strategy (Fig. 5g). Taken together, these results show that low 5HT in V1 predicts better task performance.

We next estimated behavioral attention from FA licks preceded by either high or low GRAB5HT signals (Fig. 5h). We saw qualitatively similar effects on weighted sensitivity maps (Fig. 5i-j) and observed a ~40% larger average attention index during GRAB5HT low as compared to GRAB5HT high (Fig. 5k). Again, these changes in attention index based on the GRAB5HT signal arose without any change in spatial sensitivity maps for vertical and contrast energy (Supplementary Fig. 6b-c). However, the GRAB5HT cohort showed less organized sensitivity maps for vertical energy than those from our photometry and optogenetic cohorts. We previously showed this variability arises because individual mice can choose to monitor contrast energy, vertical energy, or both energies37; our GRAB5HT cohort appeared to favor contrast more than vertical energy but this bias was unrelated to 5HT release (Supplementary Fig. 6b). We conclude that 5HT release dynamics in V1 are tightly linked to how strongly a mouse attends but are not related to its spatial sensitivity to stimulus features.

Methods
Animals
Male and female Cdh13-CreER (Cdh13Ce), ePet1-Cre (ePet1), Ai95 and Ai27 mice were used in this study. Mice were housed in the Goodman Cancer Research Center Vivarium on a 12hr light/dark cycle using standard room temperature and humidity requirements. Mice for behavioral studies were reverse cycled. Rosa26-LSL-ChR2-tdTomato (AID27, RRID:IMSR_JAX:012567) and Rosa26-CAG-GCaMP6f (RRID: IMSR_JAX:028865) mice were purchased from the Jackson Laboratory and crossed with ePet1-Cre mice or Cdh13-CreER for recording and optogenetic experiments, respectively.

We injected tamoxifen to drive selective expression in Cdh13-CreER (i.e. GCaMP, ChR2, Jaws). Cre expression in the ePet1 line was constitutive. All surgical and experimental procedures were in accordance with the rules and regulations established by the Canadian Council on Animal Care, and protocols were approved by the Animal Care Committee at McGill University.

Tamoxifen
Tamoxifen (TMX) was dissolved in anhydrous ethanol at 200 mg/mL, diluted in sunflower oil to 10 mg/mL, sonicated at 40ºC until dissolved, and stored at –20°C as previously described90–92. Prior to injection, TMX aliquots were heated to 37°C and a single injection was delivered intraperitoneally at ~1g/50g body weight to Cdh13-CreER ~P30 mice.

Behavioral arena
We used the same behavioral arena as described previously37. Briefly, the behavioral apparatus consisted of a custom built soundproofed dark box that displayed visual stimuli via two 60 Hz LCD screens displayed at a 32-degree angle from the mouse’s midline. The mouse was head fixed in the center of the apparatus on a platform and a lick spout positioned to administer liquid reward. Licking was capacitively sensed and digitized using custom electronics. Liquid reward delivery was controlled with a solenoid pinch valve driven by custom electronics. The behavioral task and presentation of visual stimuli was controlled using MATLAB’s psychophysics toolbox (Psychtoolbox3), and custom data acquisition software.

Behavioral task design
We trained mice in our task as described previously37. Briefly, mice performed a spatially cued detection task and were trained to lick in response to a 3-bar grating embedded within a noisy checkerboard background. The grating coherence was parametrically controlled by altering the number of noisy checkers that the 3-bar grating contained (Fig.1b). At a grating coherence of 1, the 3 bars contain no noisy checkers, although checkers surrounding the 3 bars are always noisy. At a grating coherence of 0, all checkers are pure noise. While the 3 vertical white bars in the grating increased vertical and local contrast energy, the total number of white checkers in the checkerboard stimulus was always 50%.

Trials began with a sided auditory cue and a static random checkerboard. Mice would lick to start the dynamic checkerboard noise. The coherent grating then occurred on the cued screen at a random time, chosen between 3-12 seconds. For each individual trial, the wait time before the coherent grating appeared was randomly chosen to produce a constant probability of appearing37 (i.e., a flat hazard function).

A lick that occurred within 0.1 - 1.5 seconds following the 3-bar grating was labeled as a hit and led to rewards. The absence of licks during this window were misses and were not rewarded. If mice licked before the onset of the 3-bar grating (false alarm), the trial was restarted. Restarts caused the audiovisual cue to reappear for 0.5s, followed by a return of dynamic checkerboard noise.

Training schedule
Reverse-cycled (12hr/12hr inverted light-dark) mice were trained as previously described37. Briefly, water restricted mice were habituated to behavioral arenas for 2 days prior to behavioral shaping. Our shaping protocol is as follows: (1) 1 day to associate licking with liquid reward; (2) 4 days to associate strong 3-bar gratings (coherence > 0.8) with reward; and (3) 7-10 days to lengthen maximum wait times to 12 seconds and decrease grating coherences. By session 15, mice produced reliable psychometric curves. A typical training session lasted 1.5-2 hrs. Mice were never rewarded for false alarm licks.

Behavioral analysis of grating detection
For photometry experiments, we pooled trials from later sessions (>session 15) and divided them into DR-low and DR-high categories before computing psychometric measures of performance. For optogenetic studies, control datasets consisted of 10-15 sessions prior to LED stimulation; experimental datasets consisted of all trials during LED stimulation.

Psychometric functions for hits as a function of grating coherence were described by a Weibull function, which provided four parameters: mean, threshold, response bias (lower asymptote), and lapse rate (higher asymptote). Here, threshold represents the grating coherence that is detected at 50% rate of the total response range. Signal detection theory was used to compute the discriminability index d-prime = Z(PH)−Z(PH0) and criterion = − 0.5 X (Z(PH)+Z(PH0)) / d-prime, where PH is the probability of a hit at a single grating coherence. PH0 is the probability of a lick to a zero-coherence grating, and Ζ is the inverse of the standard normal cumulative distribution function. For example, if a mouse were to disregard the visual stimulus and randomly lick, then PH0 and PH would be the same and d-prime = 0 for all coherence levels.

Reaction times were described by a standard exponential function: ae−b (Coherence) +c, with amplitude (a), baseline (c), and slope (b).

Quantification of behavioral sensitivity and attention
Our previously published logistic model revealed that attention multiplicatively scaled mouse behavioral sensitivity for grating-like features on the cued versus the uncued side37. Our current model simplifies this approach by capturing this attentional scaling in a single parameter. Briefly, we used false-alarm licks preceded by at least 1-3 seconds of lick-free random checkerboard noise from the final 20 sessions of mice. This removed FA lick “bouts” and FA licks occurring before the reaction time window but after grating presentation.

Features were computed as follows: Vertical stimulus energy was computed at the same spatial frequency as the target stimulus (0.077 cycles per degree) by convolving quadrature phased Gabor filters (26x26 checkers, S.D = 6 checkers or 3 deg of visual angle) with each stimulus frame. Contrast energy was computed by convolving each stimulus frame with a Gaussian filter (S.D. = 4 checkers, 2 deg).

Resulting energy maps for the two features were then down sampled in time from 30 Hz to 10 Hz and spatially down sampled from 17x26 checkers to 3x5 checkers to ease computational load. Energies for both features were z-score normalized.

FA licks were modeled using the logistic function:
(1) P = 1 / (1+e−∑ fβf,xy (αCued ECued,f (t−δ)+ αUncued EUncued,f (t−δ))−k)
In this equation, the probability of a FA lick (P ) is a function of z-scored stimulus energy features (ECued,f and EUncued, f), where (f) is the vertical or local contrast. In this model,  is the time it takes for stimulus energy to produce a FA lick, chosen a s a hyperparameter based on each individual mouse’s psychometric reaction time distribution; we previously showed individualized reaction times for mice37. We optimized 3 unknown model parameters: βf,xy is the sensitivity of FA licks to the stimulus energy on both the cued or un cued screen for each individual checker at location (x, y) for each feature (f); 𝜃 is the gain parameter (constrained to [0, /2]) which determines the weighting of cued and uncued stimulus energy (ECued,f and EUncued, f), and k is the stimulus energy corresponding to P = 0.5. The weighting of cued and uncued energies were captured by the relationship:
(2) αCued = sin(𝜃)and αUncued = cos(𝜃) (also see Fig. 1d).

The model parameters can be interpreted as follows:  is a spatial distribution of behavioral sensitivity applied to both the cued and uncued screens, and 𝜃 is the gain parameter that scales energies from the cued side if 𝜃 > 𝜋/4, such that αCued > αUncued. Both  and 𝜃 were optimized to maximize the model predictive power over FA behavior (Supplementary Figure 1a). Gain 𝜃 = pi/4 means no bias to cued features over uncued features, and 𝜃 = pi/2 is 100% bias to cued features over uncued features.

We treated the model as an optimization problem for a constrained nonlinear multivariable function, implemented using MATLAB’s fmincon with the negative-log-likelihood cost function:
(3) NLL = argmin ∑−[log (σ(y = 1)) + log (1−σ(y = 0))]−λ∑w2,
where  is the regularization factor for standard L2 regularization, w are the parameters , y are the n observations (lick or no lick), and  the standard logistic function. Each model was 5-fold cross-validated to optimize hyperparameters without bias. Model predictive power was measured using the area under the receiver-operator-characteristic.

Normalization Model of Attention
To understand how DR-5HT may contribute to suppressive drive in the normalization model of attention44, we modified the model (http://www.cns.nyu.edu/heegerlab/) directly by introducing a single parameter that multiplicatively scales suppression drive, such that:
(4) R = E / (εS+σ),
where R is the population response, S is the suppressive drive, E the excitation field,  the constant that determines contrast gain. DR-5HT low was modeled ε = 0.0002 and σ = 3×10-7, with other parameters adopted from the original model code. DR-5HT high was modeled with the exact same parameters except ε = 0.003, thereby increasing the effective suppressive drive (Fig. 6).

Fiber optic implants
For photometry and optogenetic studies, mice were anesthetized (isofluorane, 4% induction, 1-2% maintenance), mounted on a stereotaxic frame and eye ointment was applied to prevent corneal drying. Following lidocaine application and subcutaneous carprofen injections, a circular incision was made, and the underlying fascia cleaned to dry the skull. Bregma and lambda were then levelled to within 0.1 mm of one another. A 0.1 mm craniotomy was performed above the DR, and the fiber implant (400 m diameter fiber, ThorLabs) was slowly inserted using a stereotactic arm (4.7 mm posterior from bregma, on the midline and 3.1 mm below the pia). Metabond cement was applied around the implant location and a headplate was cemented additionally to the skull. Any remaining exposed skull was sealed using cement. Following surgery, the animal was removed from the stereotaxic frame and placed on a heat pad for recovery, and analgesia was administered subcutaneously following surgery for 3 days. Shams received implants but did not express optogenetic constructs.

Cranial window
For GRAB5HT imaging in V1, mice were anesthetized and mounted on a stereotaxic frame as described above but received a 5 mm diameter craniotomy over the left V1 followed by a circular glass coverslip (4 mm diameter) centered 1 mm rostral from lambda and 2.5 mm lateral from the midline. Cranial windows were fixed using instant glue and Metabond at a 15⁰ angle to the mediolateral axis and mice then received a metal headplate implant that surrounded the craniotomy.

Injections
AAVs were intracranially injected using previously described methods93,94. Mice used in the optogenetic suppression experiments received a 1 l injection of AAV1-CAG-FLEX-rc [Jaws-KGC-GFP-ER2] in the DR (4.7 mm posterior from bregma, on the midline and 3.3 mm below the pia). Mice for 5-HT imaging received three 500 nl infusions of AAV9-hsyn-GRAB_5-HT1.0 in the left visual cortex (centered around 1 mm rostral from lambda, 2.5 mm lateral from the midline at a 30⁰ injection angle and 0.2 mm below the pia). A microsyringe pump (UMP3-4, World Precision Instrument, Sarasota, FL) was used to infuse the virus at 5 nL/s.

Viruses
AAV9-hsyn-GRAB_5-HT1.0 (Cat# 140552-AAV9) and AAV1-CAG-FLEX-rc [Jaws-KGC-GFP-ER2] (Cat# 84445-AAV1) were purchased from Addgene and used for intracranial injections.

Photometry
A 1-site custom-built fiber photometry system was used to assess changes in GCaMP6f fluorescence from DR neurons. Calcium insensitive fluorescence was measured with 405 nm light oscillating at a carrier frequency of 450 Hz. Calcium sensitive fluorescence was measured with 472 nm light oscillating at 205 Hz. An optical fiber (ThorLabs) was used to both deliver and collect light from the brain. Light emitted in the 500-550 nm band was measured using a femtowatt photoreceiver and digitized 200Hz with an RX8 signal processor (Tucker Davis Technologies). The recorded signal was demodulated according to the two carrier frequencies and low pass filtered at 20 Hz. Calcium signals were discretized into individual sessions and calcium insensitive recordings were fit to the calcium-sensitive signal using least-squares regression using a 5-minute moving window. The fitted calcium insensitive signal was subtracted from the total signal (Supplementary Fig. 1c-d). The resulting signal was normalized to the mean of each session via z-scoring to yield a final measure of ΔF/F.

Wide-field imaging through cranial window
We used a custom-built epifluorescent microscope to image through our cranial windows with a 10 Hz sampling rate at 512x512 pixel resolution. Since windows were mounted at a 15-degree angle with respect to the mouse’s medio-lateral head axis, we mounted the microscope at the same angle such that mouse heads remained leveled with respect to the visual stimuli. GRAB5HT fluorescence was measured with a 472 nm excitation LED and LabVIEW custom code. Session signals were z-scored pixel-wise to yield a final measure of ΔF/F. Signals in Fig. 5 are pooled averages of the brightest 10% of pixels in the image.

Photometry signal processing
To explore how DR activity on short timescales affected animal performance in our detection task, we first high pass filtered our ΔF/F session signals recorded via fiber photometry or widefield imaging with a cutoff frequency of 1/40 Hz (Supplementary Fig. 1c-e). Since only hit trials contained reward events that accompanied large positive DR reward signals (see Fig. 2f-g), a filter may artificially introduce baseline shifts preceding the grating presentation on hit trials, but not on miss trials. To eliminate these potential baseline artifacts preceding grating presentation, we removed reward signals from our data before filtering (Supplementary Fig. 1g). Doing so ensured that hits and misses were treated the same. We then computed the average baseline activity in a window preceding grating onset for each trial; this time window was half the length of the unique delay period for each trial. Trials below the median baseline activity of all trials were categorized as DR low, trials above the median were categorized as DR high. We further excluded 5% of the trials closest to this median to ensure the absence of baseline-changing artifacts. False alarms were similarly grouped into DR low and DR high based on the mean activity of 1.5 seconds preceding the false alarm.

For long time-scale analysis (Supplementary Figure 3), session signals were low pass filtered using a 5 Hz cutoff frequency and a subsequent rolling mean with a window length of one minute was applied. Trials in the session were then identified as DR low if most of the trial signal (80% of data samples) remained below the median of the session signal. Trials above the session signal were labeled as DR high.

Optogenetics
We developed custom code on an Arduino Due to drive a high-powered LED (Thorlabs) that was attached to the mouse’s implant through an optical fiber (400m diameter, Thorlabs). Our custom behavioral code was adjusted to have full control of the LED stimulation time. For experiments involving mice expressing ChR2, we drove a 470 nm LED at 20 Hz (150 ms pulse duration, 17.2 mW). Mice carrying AAV1-Jaws received a constant 595 nm LED input (8.7 mW) for the duration as suggested previously95. Mice were trained fully on the visual detection task for 30-40 sessions, the last 10-15 of which constituted the control sessions (see Ctrl, Fig. 4e-n). Trained mice then received LED stimulation on every trial for at least 15 more sessions. LED stimulation began 2 seconds before the grating stimulus and was scheduled for a duration of 3 seconds. False alarm licks interrupted ongoing LED stimulation and led to trial restarts as described above.

Histology
Mice were euthanized by isoflurane overdose and perfused intracardially with phosphate buffered saline (PBS), followed by 4% paraformaldehyde (PFA) dissolved in PBS chilled to 4 degrees. Brains were removed and postfixed in 4% PFA overnight. Next, brains were embedded in 2.5% agarose (Sigma), mounted on a tissue slicer (Compresstome), and 150 um thick coronal brain sections were sliced and stored in PBS until immunostaining. Brain sections were incubated with blocking buffer (10% normal donkey serum, 0.4% Triton X-100 in PBS for 1-2 hours), then incubated for 7 days at 4˚C with primary antibodies, and with secondary antibodies overnight at 4˚C. Primary antibodies were used as follows: rabbit anti-TPH2 (1:1000, Novus; 1:500, Synaptic Systems); chicken anti-GFP (1:1000, Abcam). Secondary antibodies were conjugated to Alexa Fluor 488 (Cederlane), or Cy3 (Millipore). Sections were mounted onto glass slides using Vectashield (Vector Lab) or Fluoromount (Millipore) mounting medium.

All antibodies were used at dilutions recommended by the manufacturer: Rabbit anti-TPH2 (Polyclonal, Novus, 1:1000, Cat. No.: NB100-74555); Rabbit anti-TPH2 (Monoclonal, Synaptic Systems, 1:500, Cat. No.: 348 003); Chicken anti-GFP (Polyclonal Abcam, 1:1000, Cat. No.: ab13970); Donkey anti Chicken Alexa Fluor 488 (Cedarlane, 1:1000, Cat. No.: 703-545-155); Donkey anti Rabbit Cy3 (Polyclonal, Millipore, 1:1000, Cat. No.: AP182C); Experiments were performed using a single lot per antibody.

Immunostained images were acquired from a Zeiss confocal microscope, using 405 nm, 488 nm, 559 nm, and 635 nm lasers. ImageJ (NIH) software was used to analyze confocal stacks and generate maximum intensity projections to one image.

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "In the article “Rapid dynamics of dorsal raphe serotonin neurons selectively regulate the gain of visual attention”, Lehnert and colleagues are investigating the role of the Dorsal Raphe serotoninergic (DR-5HT) neurons in the mouse performance at a visual detection task. In the task, mice are placed head-fixed in front of two screens covering the righty and left visual field respectively. An audiovisual cue indicates on which screen the rewarded cue will be presented. Then, a dynamic checkerboard noise is shown. The rewarded stimulus (a 3-bar vertical grating) is presented at a random time during this noise, and the mouse must lick to obtain the water reward. The authors find that the mouse will be more likely successful at detecting the rewarded stimulus presented during the checkerboard noise when DR-5HT neurons are less active. Using a model, they found that false alarms (FA) are more likely to be triggered by events of the checkerboard noise that could look like the rewarded stimulus (enhanced vertical and contrast energy). Defining attention is tricky and varies from lab to lab. Here, attention is defined as a modulation of the gain of neurons of the primary visual cortex (V1) encoding for the rewarded cue. The main hypothesis of the authors is that the DR-5HT neurons modulate the gain of V1 neurons in the attended visual field. This idea is very interesting, yet some important elements of the demonstration are missing to be convincing.\n1. Laterality. The claim that DR-5HT activity supports attention is V1 is supported by a model that shows that false alarms (FA) are more likely to occur when events in the checkerboard noise that are compatible with the rewarded cue by their vertical and contrast energy are presented on the screen that was cued as being the one where the rewarded stimulus will be presented. It is to be noted that the predictive power of the model is low (the area under ROC is between 51 and 56%, Ext data Fig., 1). Yet, following the hypothesis that DR neurons modulates the gain of the V1 neurons that are expected to provide the behaviorally relevant information (i.e., neurons located in the V1 contralateral to the cued screen), we could expect to see that the neurons of the right and left hemisphere DR would be differentially active when the cue is indicating that the grating will be shown on the right or the left screen respectively. Yet the fiber photometry recording in the study averages the activity of all the DR neurons. The authors are simultaneously recording the neurons of the right and left DR. Therefore, it is not clear if the DR activate the V1 corresponding to where the rewarded stimulus will be presented or just provide the same nonspecific signal to the two V1. Therefore, I would be interested in seeing the difference in serotonin release in V1 (experiment related to Fig. 5) when the task cue indicates that the stimulus will be presented in the ipsi- and contralateral side, and to perform recording with a single cell resolution in DR to determine the diversity of activity in DR neurons. Showing that DR neurons targeting V1 and frontal cortices present different type of activity during the task would be the best.\n2. Specificity. The study is mainly interpreted through the prism of a modulation of the gain of V1 neurons. Yet, the activity of the neurons in V1 is not recorded. The evidence of a modulation of the gain of V1 neurons and how this modulation favors the response of V1 neurons to noisy stimuli that are compatible by their vertical and contrast energy with the rewarded cue during FA needs to be provided. Recording the neurons in V1 during the task and determine if the gain mechanism hypothesized by the authors can be confirmed experimentally would considerably strengthen the study. Moreover, to demonstrate the specificity of the DR-5HT activity in V1 (i.e., that the putative change in the gain of V1 neurons is not indirectly due to a DR-5HT modulation of the activity of neurons of the frontal areas providing feedback to V1), the authors should measure the release of 5HT in the frontal cortex. The anterior cingulate cortex could be a good target as it was showed to play a role in spatial attention in V1. If some DR-5HT modulation was to be found in the prefrontal cortices, the authors would have to demonstrate that the changes in detection sensitivity cannot be explained by the top-down information provided to V1 by those frontal areas.\n3. Slow versus fast DR-5HT dynamics. The authors show that there are two kinds of dynamics in the DR-5HT activity. A slow component (Ext Data Fig. 3) and a fast component (Fig.2). Are those two components also found in the release of 5-HT in V1? As those two components seems to have the same effect on detection, I would assume that a short decrease of DR-5HT as in Fig.2g is less effective when it occurs in the rising phase of the slow fluctuation (like for the Hit trial in the inset of Ext Fig. 3a). Is it the case?\n4. Locomotion and arousal. Locomotion (and movement in general) as well as arousal (typically measured by the pupil size) have been shown to play a major role in modulating the gain of V1 neurons. It would be therefore important to demonstrate that the results of the authors cannot be explain by those factors.\nMinor points.\nHow was the D prime computed? What maximal D prime can be obtained by a mouse performing the task at random?\nGratings switch sides every 35 trials. Isn’t this rule undermining the role of the cue indicating where the rewarded stimulus will be presented? Can mice perform the task if the rewarded stimulus is randomly presented on the right and left screen?", "reviewer_2": "Lehnert et al present a very nice study revealing in mice that fast activity of dorsal raphe serotoninergic neurons selectively regulate attentional gain but not spatial allocation in a visual attention task. The authors used a cleverly designed cued visual detection task that was previously developed by the lab to detect 3-bar gratings embedded in zero-coherence dynamic checkerboard noise. Such task design allows authors to use vertical and contrast energy in the visual stimuli and behavioral performance to compute attentional gain the allocation. With the task, the authors used optic approaches to record and causally manipulate (both activate and inhibit) neuronal activity in dorsal raphe nuclei, and found that increased peri-detection raphe activity correlated and causally contributed to reduced attentional gain, whereas decreased raphe activity contributed to elevated attentional gain. Crucially, they found that contribution of 5-HT on attention was specific to attentional gain control, rather than alter its spatial allocation or other aspects of attentional performance, e.g. lapse rate, criterion or reaction time. Lastly, they found in visual cortex V1 5-HT low or high release preceding grating appearance correlated high or low task performance respectively, resembling their DR photometry data. The study provides crucial mechanistic insights on how fast dynamics of 5-HT release regulate visual attention. The experiments were soundly designed and conducted, data analysis was rigorous, and text was well-written. Overall, I really like the study and enjoying reading the manuscript. I believe the study will generate great interests in the entire vision community. I only have a few minor comments that I would like authors to clarify to improve the manuscript.\nMy specific comments:\n1. The conclusion that 5-HT modulates attentional gain but not focus was based on attention maps constructed with vertical and contrast energy in the visual stimulus and FA licks during different 5-HT levels or manipulation conditions. I feel this might be a bit overstated, given the task design and data shown. In the task, the cue was 100% valid and the 3-bar gratings were always on the center of the cued screen. Therefore, it seems to be a given that high sensitivity to vertical or contrast energy remained to be at the center of the screen regardless of 5-HT levels. What could be other potential scenarios in the attention map? Perhaps authors could instead look into a subset of trials at the beginning of cue/block switch, where spatial predictiveness of the target is at the lowest to reveal whether 5-HT level is indeed involved in attentional allocation.\n2. The visual stimuli were lateralized in the task. Was the involvement of 5-HT in attentional modulation lateralized? Photometry and optogenetic stimulation in DR in the study appear to cover both hemispheres, however, 5-HT wide-field imaging in V1 was done unilaterally. Does the predictiveness of V1 5-HT in attention performance depend on laterality of the cue?\n3. The authors appear to fixate that 5-HT modulation on visual attention is through V1. Indeed, authors found 5-HT dynamics in V1 resembled that of DR photometry recordings. However, such correlation was not causal. Optogenetic stimulation of DR can affect 5HT release in many other downstream targets. Besides V1, DR-5HT neurons also densely project to superior colliculus (SC) and exert potent suppression of SC visually evoked activity. Given SC is involved in visual attention, including in mice, it is plausible that 5-HT release in SC is playing an even more prominent role in attention. Ideally, optogenetic manipulation of 5-HT axon terminals in V1 could provide a definitive answer. The authors should at least discuss 5-HT release in other visual areas in attentional control.", "reviewer_4": "In this manuscript, Lehnert et al. show that 5-HT modulates visual attention, with decreases in 5-HT neural activity and release in V1 resulting in greater attention towards visual cues. The authors employ a useful behavioral paradigm for tracking attention in addition to fiber photometry, optogenetics and widefield imaging to track and manipulate DR neurons/5-HT release during execution of this task. Consistent findings across these techniques are a strength of the manuscript. The data could be more clearly presented and described.\nShowing fluorescence data without filters applied would be a useful supp figure given the authors noted caveat the filters can change activity around large deviations (even if the authors removed the Hit activity prior to the filter, it would be good to show less processed data that show this to be the case)\nWhy is the baseline attentional index data for the optogenetic cohorts much lower than either recording cohort? And the attentional index equation/explanation should be included in text not just figure.\nAre there differences in behavior and/or 5-HT activity in trials that follow a FA vs trial start lick?\nFigure 3 d prime and threshold plots should start at 0 on the y axis.\nMinor comments:\n-5HT recording data at the level of V1 does not correlate as robustly with attention as photometry data, and gain/loss of function data is at the level of the DR. This limits the ability to state that the 5-HT modulation is at the level of V1 (or at least only at the level of V1).\n-In some supp figure legends delta f/F is written as delta f/Ff\n-Colors in supplemental figures often re-use blue/orange color scheme despite not referring to the same features as the main figures (e.g., Figure S3 uses blue/orange for hit/miss and has entirely new colors for high/low DR activity). Figures should have consistent themes to help readability."}, "author_response": "Response to reviewer 1\nIn the article “Rapid dynamics of dorsal raphe serotonin neurons selectively regulate the gain of\nvisual attention”, Lehnert and colleagues are investigating the role of the Dorsal Raphe\nserotoninergic (DR-5HT) neurons in the mouse performance at a visual detection task. In the task,\nmice are placed head-fixed in front of two screens covering the righty and left visual field\nrespectively. An audiovisual cue indicates on which screen the rewarded cue will be presented.\nThen, a dynamic checkerboard noise is shown. The rewarded stimulus (a 3-bar vertical grating)\nis presented at a random time during this noise, and the mouse must lick to obtain the water\nreward. The authors find that the mouse will be more likely successful at detecting the rewarded\nstimulus presented during the checkerboard noise when DR-5HT neurons are less active. Using\na model, they found that false alarms (FA) are more likely to be triggered by events of the\ncheckerboard noise that could look like the rewarded stimulus (enhanced vertical and contrast\nenergy). Defining attention is tricky and varies from lab to lab. Here, attention is defined as a\nmodulation of the gain of neurons of the primary visual cortex (V1) encoding for the rewarded\ncue. The main hypothesis of the authors is that the DR-5HT neurons modulate the gain of V1\nneurons in the attended visual field. This idea is very interesting, yet some important elements of\nthe demonstration are missing to be convincing.\nWe are happy the Reviewer finds the central idea of our paper interesting. Before we respond to\nthe reviewer’s individual points, however, we feel that a clarification of our use of the term “gain”\nis in order. In our study we modeled the behavioral allocation of attention using multiplicative gain\nterms ( and  in Manuscript Fig. 1d) and found DR-5HT was linked to changes in these\ncued uncued\n1\nparameters. Our behavioral gains (), however, are not intended to describe the neural\nmechanism of attention in V1 or other brain areas. Thus, our results make no claims on whether\nDR-5HT directly affects affect neural gain at the level of V1. We recognize that our use of the term\n”gain” to describe the behavioral effects of attention lacked clarity because changes in the gain of\nneural tuning is one of the mechanisms by which attention exerts its effects. We have now\nemphasized this point throughout the manuscript and changed our title.\nRev1.1) Laterality. The claim that DR-5HT activity supports attention is V1 is supported by a model\nthat shows that false alarms (FA) are more likely to occur when events in the checkerboard noise\nthat are compatible with the rewarded cue by their vertical and contrast energy are presented on\nthe screen that was cued as being the one where the rewarded stimulus will be presented. It is to\nbe noted that the predictive power of the model is low (the area under ROC is between 51 and\n56%, Ext data Fig., 1).\nYet, following the hypothesis that DR neurons modulates the gain of the V1 neurons that are\nexpected to provide the behaviorally relevant information (i.e., neurons located in the V1\ncontralateral to the cued screen), we could expect to see that the neurons of the right and left\nhemisphere DR would be differentially active when the cue is indicating that the grating will be\nshown on the right or the left screen respectively. Yet the fiber photometry recording in the study\naverages the activity of all the DR neurons. The authors are simultaneously recording the neurons\nof the right and left DR. Therefore, it is not clear if the DR activate the V1 corresponding to where\nthe rewarded stimulus will be presented or just provide the same nonspecific signal to the two V1.\nThe reviewer asks if our recorded DR-5HT signals have a lateral component. In our attempt to\nsimplify the logic of our writing, we may have inadvertently suggested that the DR-5HT attention\nsignal must be laterally implemented in V1. It does not.\nWe explicitly modelled behavior as a set of spatial sensitivities to vertical and contrast energy (\nv\nand  ) and multiplicative gains to cued and uncued screens ( and  , see Manuscript\nc cued uncued\nFig. 1d). Our results showed that lower activity in the DR and lowered release of 5HT in V1\nfavored behavioral gain to the cued side. Our results do not address, however, how lowered 5HT\nrelease in V1 implements this behavioral change.\nThe reviewer suggests one possible scenario: a selective reduction in 5HT within the V1\ncontralateral to the cued screen increases the gain of visual neurons. Another scenario is that\nlateralized reductions in 5HT could happen in other brain targets relevant to visual attention which\nreceive strong input from DR (ie: frontal cortex, dLGN, etc).\nImportantly, the release need not be lateralized at all; DR-5HT could instead gate or increase the\nefficacy of ongoing neural gain mechanisms that are themselves lateralized. Prior work would\nfavor this latter view because (1) DR-5HT neurons encode internal states rather than external\nspatial locations1–6; and (2) because there is no evidence that DR projections to cortex are\nlateralized7.\nWe feel there are two major mechanistic clues: (1) recent observations that DR-5HT neurons\nencode expected uncertainty5,6 and (2) older observations suggesting that expected uncertainty\ntracks cue validity8–13. DR-5HT is low when the outcome (or uncertainty therein) is predicted by\nthe cue and is high when unexpected shifts (eg: block switches in our task, Reviewer Fig. 8a)\nsteal predictiveness from the cue. In this scheme, the brain has methods to lateralize visual\nprocessing in response to the cue but only does so when the DR-5HT signal deems the cue\nimportant.\nWhatever the mechanism is, prior work suggests that the spatial and temporal patterns of 5HT\nrelease across the cortex are diverse3,14–16. Moreover, the expression of the 14 different 5HT-\nreceptor isoforms across the cortex17–21 (including V1) will require significant work to tease apart\nand relate to the attentional effects we have reported here. We are currently working on this issue,\n2\nbut to acknowledge the reviewer’s interest, we have included some preliminary widefield\nGRAB5HT mesoscale imaging data from the entire dorsal cortex in Reviewer Fig. 1.\nRev1.2) Therefore, I would be interested in seeing the difference in serotonin release in V1\n(experiment related to Fig. 5) when the task cue indicates that the stimulus will be presented in\nthe ipsi- and contralateral side,\nBriefly, we used established procedures22 to deliver php.Eb AAVs23 containing genetically\nencoded 5HT sensors (GRAB5HT24,25) to mice and then mesoscale widefield imaged sensor\nfluorescence over the entire cortex while mice performed our task (Reviewer Fig. 1a). This\npreliminary widefield dataset confirms our observations from photometry and cranial window\nimaging (Manuscript Figs. 2-3,5) and shows that 5HT release drops in both V1s before hits\n(Reviewer Fig. 1b). Correlating GRAB5HT signal across hemispheres (Reviewer Fig. 1c, top)\nshows that both V1s receive similar but not identical signals (Reviewer Fig. 1c, bottom). This\nReviewer Figure 1. a, Schematic of the task. b, Sample widefield image of cortex-wide GRAB5HT\nfluorescence with atlas showing cortical regions (left) and both left and right V1 (right, n=2 mice). c,\nExample GRAB5HT fluorescent signals in cortical pixels located at mirror symmetric positions across the\nmidline and their correlation (left) and interhemispheric correlation for all mirror symmetric pixels from two\nmice (right, n=2 mice shown).  is 0.64 in the visual areas. d, Average maps of cortical GRAB5HT\nsurrounding grating appearance on hit trials (left) and rasters for one mouse, showing neural signals within\nthe indicated cortical regions. 5HT release is low in posterior visual (dashed boxes) and midline frontal\nareas prior to a hit, whereas it is high in somatosensory areas over the same period. e, Same as d, but for\nmisses.\ninterhemispheric similarity was also observed for many other cortical areas (Reviewer Fig. 1d-\ne). These observations suggest that DR sends a global signal to both visual cortices, thereby\nenhancing ongoing lateralized mechanisms that embody attentional focus. While circuit-level\ndetail about such focusing mechanisms remains at large, they could involve input from frontal\ncortical areas described in recent studies14,26.\nRev1.3) …and to perform recording with a single cell resolution in DR to determine the diversity\nof activity in DR neurons. Showing that DR neurons targeting V1 and frontal cortices present\ndifferent type of activity during the task would be the best.\nThis comment arises from the view that DR-5HT signals are responsible for changes in neural\ngain of V1. We respectfully point out that we never claimed this. Nonetheless, the reviewer is\nrequesting that we (1) perform cell-type specific extracellular recordings from DR neurons in vivo,\nand (2) assess whether DR neurons targeting V1 or other areas present different activity patterns.\n3\n(1) Type-specific recording of DR diversity: We currently do not have the capability to record from\nDR neurons; purchase and integration of this equipment into our behavioral setups would take\ntime. This said, there is extensive work on the molecular taxonomy of DR neurons which show 5-\n7 major classes with at least 10-14 molecularly distinct subtypes3,15,27. These same studies also\nexamined neural activity in select DR-5HT subtypes which together with older work1,2,4,16,28,29\nsuggests a diversity of functional responses across different kinds of DR-5HT neuron. Taken\ntogether, this data indicates substantial functional diversity in DR-5HT neural populations.\nWhile we cannot provide extracellular recordings from genetically defined DR-5HT subtypes, our\nanalysis of widefield GRAB5HT imaging during hits already shows some diversity in how 5HT is\nreleased across cortex (Reviewer Figures 1&2). The rapid drop in 5HT levels that we show\nregulates behavioral attention occurs mainly in posterior visual and midline frontal areas (e.g.\ncingulate) before hits. However, we see a rise in 5HT levels over the same period in\nsomatosensory, auditory, and lateral motor regions (Reviewer Fig. 1d).\nGiven this observation, we decomposed our widefield imaging data into its principal components\nto visualize spatial patterns of GRAB5HT signal that best characterize our data. These\ncomponents were highly diverse. The top 5 patterns reveal both brain-wide and region-specific\npatterns of release (Reviewer Fig. 2). PC2 is spatially like the pattern seen during hits, whereas\nPC4 shows that visual areas experience different pattern of 5HT release than the rest of the brain.\nTaking the results in both Reviewer Figs. 1-2 together, the spatiotemporal patterns of 5HT release\nacross cortex are diverse and not strongly lateralized.\nReviewer Figure\n2. a, Principal\ncomponents\n(PCs) obtained\nacross all trials\n(n=1 mouse)\nduring our task\nand a bar graph showing variance accounted for by each PC. b, Brain maps of the top 5 PCs. PC2\nresembles the pattern seen before hit trials, whereas PC4 indicates that V1 can receive different patterns\nof 5HT release than the rest of the brain.\n(2) Do DR cortical projections present different activity patterns? Details about the projection\npatterns of every DR type is an area of ongoing research. Evidence is limited to a set of recent\nstudies which used injections of retrograde viral tracers in target structures paired with single-cell\nsequencing in the DR to define how a given DR type projects3,15,16,27. These studies show that\ncortically projecting types lie directly in the midline under our optical fiber, whereas subcortically\nprojecting ones reside in the lateral parts of DR that are likely poorly sampled by our optical fiber.\nAn elegant study by Ren et al. 2018, showed that DR types projecting to frontal areas only weakly\ninnervate posterior cortical areas3. Our widefield imaging results support this view and show\ndifferent neural dynamics in the posterior versus the anterior dorsal cortex during hits (Reviewer\nFig. 1d) and a functionally defined posterior compartment of 5HT release (PC4, Reviewer Fig.\n2b).\nIn summary, the reviewer asked that we investigate the diversity of DRN neurons using single cell\nrecordings. Based on previous studies by other labs we feel this would be a major undertaking\nbeyond our current technical abilities and outside the scope of our current study. However, our\nanalysis of hits/misses together with our PCA shown above suggests a diversity of at least 4\ndifferent cortical compartments of DR-5HT activity: midline frontal, motor, somatosensory, and\nvisual areas (Reviewer Figs.1-2). Our lab is in a good position to continue these mesoscale\nimaging studies in the future to untangle how these different cortical compartments are linked to\nthe DRN (the main source of 5HT in the forebrain) and attention.\nof V1 neurons. Yet, the activity of the neurons in V1 is not recorded. The evidence of a modulation\nof the gain of V1 neurons and how this modulation favors the response of V1 neurons to noisy\nstimuli that are compatible by their vertical and contrast energy with the rewarded cue during FA\nneeds to be provided. Recording the neurons in V1 during the task and determine if the gain\nmechanism hypothesized by the authors can be confirmed experimentally would considerably\nstrengthen the study.\nThe reviewer suggests our hypothesis is that DR-5HT signals regulate attention by modifying the\ngain of V1 neural tuning. As we mentioned above, we recognize a lack of clarity in the\npresentation of our results and the use of the term “gain”. Our study selectively links changes in\nDR-5HT to changes in behaviorally defined attentional strength.\nOur results show that a decreased DR activity and 5HT release in V1 are related to an increased\n‘behavioral gain of attention’; captured by cued and uncued model weights ( and  ).\ncued uncued\nThese results do not show how reduced DR-5HT implements this behavioral change. Our\nmeasures of 5HT release in V1 (Manuscript Fig. 5, Reviewer Figures 1-2) and many prior\nstudies30–40, suggest that V1 could be an important site of DR-5HT action. We have expanded our\ndiscussion of potential sites in the revised manuscript.\nProviding conclusive evidence and characterization of the V1 neural correlate of attention in our\ntask will take time. We are actively pursuing this line of research, although it is still in its early\nstages. To address the reviewer’s interest, we show some of our preliminary results in Reviewer\nFig. 3.\nBriefly, we expressed genetically encoded calcium indicators (GCAMP6f) across the cortex and\nwidefield imaged neural signals from the cortex while mice performed our task (Reviewer Fig.\n3a). Next, we extracted visual contrast energy from noise periods between false alarm licks and\ncorrelated contrast energy fluctuations at each checker position to the corresponding movies of\ncortex-wide GCaMP6f activity (Reviewer Fig. 3b). As expected, contrast energy correlations were\nstrongest in the visual cortex (dark spot in Reviewer Fig. 3b). The correlations between contrast\nenergy and V1 calcium signals also exhibited the expected retinotopic organization (Reviewer\nFig. 3c). Interestingly, our early analysis of these correlations when segmenting our data into cued\nand uncued checkers and brain maps reveals stronger correlations to cued contralateral contrast\nReviewer Figure\n3. a, Schematic of\nour task with noise\nsequences before\nfalse alarms\nindicated. b,\nSchematic of our\nanalysis to\ncorrelate changes\nin contrast energy\nover time to neural\nactivity in widefield\nimaging. Individual\ncheckerboards are\nconvolved with\nGaussian filters to\nextract contrast\nenergy and then\nchanges in energy\nat each checker\nposition are\ncorrelated with the entire brain map of neural activity. Correlation maps show a blob of high correlation in\nthe contralateral V1 c, Retinotopic maps obtained by taking the peak of the correlation blobs obtained from\nthe analysis shown in b. d, Correlation maps for left and right V1 when the contralateral screen was cued or\nuncued (top, middle rows) and a difference of these correlation maps (bottom row). Cueing increases the\ncorrelation between contrast energy fluctuations and visual cortical activity.\n5\nenergy fluctuations (Reviewer Fig. 3d). This example shows that left V1 had a much stronger\ncorrelation (dark) with contrast energy when the cue was on the right, while the effect of cue\nlocation on the right V1 was more mixed.\nThese preliminary results indicate that visual cortex can be more strongly driven by cued\ncheckerboard noise than by uncued noise. One interpretation is that the response gain of V1 is\nenhanced by the cue. As the reviewer is certainly aware of, however, an increased correlation\nbetween the neural response and noise stimulus shown in Reviewer Fig. 3d does not\nconclusively demonstrate a multiplicative gain change in V1 (which requires measuring the tuning\nof V1 responses). Thus, many more experiments are needed to strengthen this result and flesh\nout mechanisms. Moreover, we plan to explore how this neural correlate of attention varies if we\nexamine other visual features (e.g., orientation energy and spatial frequency), examine its cellular\nbasis with 2 photon imaging, and link these effects to behavioral performance. Should all these\nstages work out, we would be in a strong position to then study the mechanisms by which 5HT\ninteracts (or influences) these attentional changes to V1.\nAlthough we are actively addressing these interesting and important questions raised by the\nreviewer, incorporating all these experiments would go far beyond the current scope of this study\nwhich is the first to show that DR-5HT signals play a causal role in visual selective attention.\nRev1.5) Moreover, to demonstrate the specificity of the DR-5HT activity in V1 (i.e., that the\nputative change in the gain of V1 neurons is not indirectly due to a DR-5HT modulation of the\nactivity of neurons of the frontal areas providing feedback to V1), the authors should measure the\nrelease of 5HT in the frontal cortex. The anterior cingulate cortex could be a good target as it was\nshowed to play a role in spatial attention in V1. If some DR-5HT modulation was to be found in\nthe prefrontal cortices, the authors would have to demonstrate that the changes in detection\nsensitivity cannot be explained by the top-down information provided to V1 by those frontal areas.\nWe respectfully restate that our results make no claim about a gain change in V1. The reviewer\nrequests that we measure DR-5HT release in the anterior cingulate cortex (ACC) to see if this\nregion has the same modulation as we observed in V1. Although we feel this is a question worthy\nof a separate study, we have generated preliminary results that show the ACC also experiences\na rapid, transient reduction in 5HT before grating appearance on hits (Reviewer Fig. 1d). To\ninvestigate this further, we split our preliminary widefield imaging data into 5HT-high and 5HT-low\nReviewer Figure 4.\na, Example\nwidefield image of\nGRAB5HT signal\nbefore a hit (top),\na histogram of the\nmean GRAB5HT\nsignal prior to\ngrating onset in the\nleft V1, and the\naverage GRAB5HT\nsignal prior to a\nfalse alarm lick\n(bottom). Atlas of\ncortical areas is\nsuperimposed on\nthe brain map and\ntraces show\nexample pre-grating\nGRAB5HT signal\nfrom the indicated\nparts of the\nhistogram. b, Example mouse mean brain maps of GRAB5HT signal aligned to grating onset (t = 0) for 5HT\nlow and high trials (left) and corresponding rasters of the indicated cortical regions (right). c, Mean brain\nmaps of GRAB5HT signal aligned to false alarm licks for 5HT low and high (left) and corresponding rasters\nfrom the indicated cortical regions (right).\n6\ngroups (Reviewer Fig. 4a) as we did for cranial window imaging in V1 (Manuscript Figure 5)\nand then examined the time course of activity across cortical regions before hits and false alarms.\nWidefield GRAB5HT imaging confirms our observation of a novel, rapid 5HT release pattern within\nV1 (Reviewer Fig. 4b-c); as in Reviewer Fig. 1, and 5HT signals do not appear lateralized to a\nparticular V1. However, segmenting our widefield datasets according to 5HT-low or high release\nin V1 before grating onset (Reviewer Fig. 4b) and false alarm licks (Reviewer Fig. 4c) show\nsimilar patterns of release in V1, midline frontal areas including ACC, and other areas. This similar\n5HT dynamic in ACC and V1 could reflect the strong interconnection between these brain areas\nand the effects of ACC feedback to V114,26, including effects that modulate visually guided\nbehaviors26. On the other hand, while similar, ACC and V1 patterns are not identical. For example,\n5HT release remains somewhat lower in ACC prior to false alarms even though 5HT release is\nhigh in V1 (Reviewer Fig. 4c, 5HT-high). More work will be needed to quantify these differences\nand relate them to attentional and behavioral performance, and we plan to pursue these important\nquestions in the future.\nRev1.6) Slow versus fast DR-5HT dynamics. The authors show that there are two kinds of\ndynamics in the DR-5HT activity. A slow component (Ext Data Fig. 3) and a fast component\n(Fig.2). Are those two components also found in the release of 5-HT in V1? As those two\ncomponents seems to have the same effect on detection, I would assume that a short decrease\nof DR-5HT as in Fig.2g is less effective when it occurs in the rising phase of the slow fluctuation\n(like for the Hit trial in the inset of Ext Fig. 3a). Is it the case?\nThere are three points raised here: (1) The reviewer asks if 5HT release in V1 also has slow and\nfast component; (2) The reviewer suggests that fast and slow DR-5HT dynamics have the same\neffects on attention. (3) The reviewer asks if fast DR-5HT low signals are better on the rising\nphase of a slow DR-5HT signal.\n(1) Do cortical GRAB5HT signals show slow and fast dynamics? The slow and fast components\nthat we saw in our DR photometry recordings are indeed mirrored within our GRAB5HT dataset\n(Reviewer Fig. 5). Briefly, raw session fluorescence is detrended (Reviewer Fig. 5a) and then\neither high- or low-pass filtered\n(Reviewer Fig. 5b) to obtain\nfast and slow GRAB5HT signals\nrespectively (Reviewer Fig. 5c).\n(2) Do slow and fast DR-5HT\ndynamics contribute to visual\nattention? Please note that fast\nDR-5HT dynamics predict\nincreased behavioral attention\ngain and performance\n(Manuscript Figures 2-3,5).\nWe now show that the same\nbehavioral measures of\nperformance are unrelated to\nslow DR-5HT dynamics\n(Manuscript Extended Data\nFigure 3). We have better\nclarified this point in our\nmanuscript.\n(3) Since behavioral\nperformance was only related to Reviewer Figure 5. a, Raw GRAB5HT signals from an example\nfast DR-5HT dynamics and not behavioral session (top) and detrended signal (bottom). b, High-\nslow ones, we predict that this and low-pass filtering applied to the example detrended session\nrelationship to fast signals will signal in a.\n7\npersist regardless of underlying slower dynamics. We nonetheless appreciate the reviewer’s\ncuriosity and now provide this analysis in Reviewer’s Fig. 6.\nBriefly, we filtered session signals as we did in our submitted manuscript to identify periods of\nslow rising and falling baseline periods (occurring over 30 sec-1 min, Reviewer Fig. 6). In parallel,\nwe high-pass filtered this signal to identify rapid DR-5HT high and low dynamics (Reviewer Fig.\n6a). Together, this allows us to analyze behavioral performance of mice during rapid DR-5HT high\nand low signals in regimes where the baseline was slowly rising or falling. As we showed in\nManuscript Figures 2-3,5 and Extended Data Figure 3, performance is only predicted by rapid\nDR-5HT high and low dynamics. Slow changes in baseline have little or no effect (Reviewer Fig.\n6b-c). We now include this new analysis in Extended Data Figure 3 of the revised manuscript.\nReviewer Figure 6. a, raw photometry signals from an entire session low pass filtered to identify rising and\nfalling baseline periods (middle) and parallel high-pass filtering to identify fast DR-high and low signals\n(bottom). b, mean psychometric curves of hits and corresponding measures of psychometric threshold,\nguess rate and lapse rate for DR-high and low trials occurring on a rising baseline. c, mean psychometric\ncurves of hits and corresponding measures of psychometric threshold, guess rate and lapse rate for DR-\nhigh and low trials occurring on a rising baseline. Slow changes in baseline are not related to detection and\nbehavioral performance (n=7 mice).\nRev1.7) Locomotion and arousal. Locomotion (and movement in general) as well as arousal\n(typically measured by the pupil size) have been shown to play a major role in modulating the\ngain of V1 neurons. It would be therefore important to demonstrate that the results of the authors\ncannot be explain by those factors.\nThere are two points here. First, the reviewer asks about the relationship between locomotion and\nour attentional and behavioral effects. Second, the reviewer asks whether the effects we saw on\nattention and behavioral performance could be explained by changes in arousal.\n(1) As in our methods, mice sat stationary on a platform during our task and therefore did not run\nduring our experiments.\n(2) We did not have the capability to record eye movements in tandem with many of the\nexperiments shown in our original submission. We now have an eye-tracker and have some\npreliminary eye data in concert with GRAB5HT data. This data is shown in Reviewer Fig.7.\nmice. Videos were then processed using facemap41 to obtain pupil ellipses (Reviewer Fig. 7a-b),\nand then pupil area extracted on hit, ignore, and miss trials (Reviewer Fig. 7a). These data show\nthat pupil areas before grating onset on hits and misses were similar, but the pupil areas were\nslightly smaller on ignore trials, consistent with prior reports suggesting that pupil dynamics\ncorrelate with arousal7.\nWe also examined the dynamics between GRAB5HT release in V1 with pupil area across a\nsession (Reviewer Fig. 7b). Plotting these traces against one another shows a largely symmetric\npoint cloud with no significant correlation (Reviewer Fig. 7c). Taken together, these data suggest\nthat the V1 GRAB5HT dynamics that we linked to attention are likely unrelated to pupil dynamics.\nWe can include some of this data as a part of an Extended Data Figure if the reviewer and editor\nfeel it is appropriate.\nReviewer Figure 7. a, Pupil areas for one mouse taken from the indicated trial outcomes. For hits and\nmisses, pupil areas come from before grating onset. All pupil dynamics were used for ignores. b, Example\nimage of a mouse eye with pupil outlined with an ellipse and time-varying changes in pupil dynamics across\nseveral trials shown (top). Example widefield image of GRAB5HT fluorescence taken from the same period\nof time as the pupil dynamics shown at top. Inset shows V1 GRAB5HT dynamics corresponding to the pupil\ntraces. c, Scatter plot of pupil area versus V1 GRAB5HT dynamics, take from traces like shown in b.\nMinor points.\nRev1.8) How was the D prime computed? What maximal D prime can be obtained by a mouse performing the task at random?\nD-prime was computed using both hit rates and false alarm rates. Briefly, signal detection theory\nwas used to compute the discriminability index d-prime = 𝑍(𝑃𝐻)−𝑍(𝑃𝐻0) and criterion =\n− 0.5 𝑋 (𝑍(𝑃𝐻)+𝑍(𝑃𝐻0)) / d-prime, where 𝑃𝐻 is the probability of a hit at a single grating\ncoherence. 𝑃𝐻0 is the probability of a lick to a zero-coherence grating (which provides the false\nalarm rate for grating detections), and Ζ is the inverse of the standard normal cumulative\ndistribution function. In our submitted manuscript we compared d-prime at a grating coherence of\n0.2.\nFor a mouse licking at random, we would expect its hit rate to be flat across all coherences. Thus,\nthe probability of a hit at zero coherence (𝑃𝐻0) is the same as the probability at any other\ncoherence (𝑃𝐻). Using the equation above returns a d-prime of zero. To improve clarity, we now\nmention this example in the Methods.\nRev1.9) Gratings switch sides every 35 trials. Isn’t this rule undermining the role of the cue indicating where the rewarded stimulus will be presented? Can mice perform the task if the rewarded stimulus is randomly presented on the right and left screen?\nCueing in blocks of trials is standard practice in many studies examining attention in humans42–\n47, non-human primates48–53, and rodents54–58. Moreover, we previously showed that mice use the\ncue to perform better in this task59. Rather than undermining the cue, the block-mode emphasizes\n9\ncue importance. We have not explicitly tried randomly presenting stimuli on left and right screens.\nWe assume that in this case, the animals would equally allocate attention to either screen because\nthe average probability of a grating to occur would be 0.5 on each screen on every trial.\nResponse to reviewer 2\nLehnert et al present a very nice study revealing in mice that fast activity of dorsal raphe serotoninergic neurons selectively regulate attentional gain but not spatial allocation in a visual attention task. The authors used a cleverly designed cued visual detection task that was\npreviously developed by the lab to detect 3-bar gratings embedded in zero-coherence dynamic\ncheckerboard noise. Such task design allows authors to use vertical and contrast energy in the\nvisual stimuli and behavioral performance to compute attentional gain the allocation. With the\ntask, the authors used optic approaches to record and causally manipulate (both activate and\ninhibit) neuronal activity in dorsal raphe nuclei, and found that increased peri-detection raphe activity correlated and causally contributed to reduced attentional gain, whereas decreased raphe\nactivity contributed to elevated attentional gain. Crucially, they found that contribution of 5-HT on\nattention was specific to attentional gain control, rather than alter its spatial allocation or other\naspects of attentional performance, e.g. lapse rate, criterion or reaction time. Lastly, they found in\nvisual cortex V1 5-HT low or high release preceding grating appearance correlated high or low\ntask performance respectively, resembling their DR photometry data. The study provides crucial\nmechanistic insights on how fast dynamics of 5-HT release regulate visual attention. The\nexperiments were soundly designed and conducted, data analysis was rigorous, and text was\nwell-written. Overall, I really like the study and enjoying reading the manuscript. I believe the study\nwill generate great interests in the entire vision community. I only have a few minor comments\nthat I would like authors to clarify to improve the manuscript.\nWe thank the reviewer for these very kind words and are delighted that they enjoyed our paper\nand believe it will generate great interest in the entire community.\nMy specific comments:\nRev2.1) The conclusion that 5-HT modulates attentional gain but not focus was based on attention\nmaps constructed with vertical and contrast energy in the visual stimulus and FA licks during\ndifferent 5-HT levels or manipulation conditions. I feel this might be a bit overstated, given the\ntask design and data shown. In the task, the cue was 100% valid and the 3-bar gratings were\nalways on the center of the cued screen. Therefore, it seems to be a given that high sensitivity to\nvertical or contrast energy remained to be at the center of the screen regardless of 5-HT levels.\nWhat could be other potential scenarios in the attention map? Perhaps authors could instead look\ninto a subset of trials at the beginning of cue/block switch, where spatial predictiveness of the\ntarget is at the lowest to reveal whether 5-HT level is indeed involved in attentional allocation.\nWe thank the reviewer for this interesting suggestion. To test this idea, we compared the mouse’s\nattentional indices and vertical/contrast energy sensitivities between two pairs of FAs: (1) FAs\npreceded by low DR-5HT occurring early in the block (after the cue switched sides) and all FAs;\n(2) FAs preceded by high DR-5HT occurring early in the block and all FAs. This comparison\nrevealed weaker attention indices early in the block for both low and high DR-5HT (Reviewer Fig.\n8a), consistent with prior work showing weaker attention following a block switch54,55,60.\nInterestingly, early-block attention indices were significantly higher for DR low as compared to DR\nhigh (Reviewer Fig. 8a). Importantly, these changes in DR-5HT activity and behavioral gain arose\nwithout effects on sensitivity to vertical and contrast energy (Reviewer Fig. 8b-c). Taken together,\nthese data indicate that DR-5HT activity levels are selectively associated with the behavioral\nstrength of attention (gain) rather than changes in the behavioral spatial sensitivity to visual\nfeatures (focus). We now include this analysis in Extended Data Figure 4 in the revised\nmanuscript.\nFAs occurring either soon after a block switch or to all FAs (n=6). Attention is weakest when DR-5HT is\nhigh during the early part of the block. b, Behavioral sensitivity to vertical or contrast energy computed from\nearly or all FAs preceded by either high or low DR activity. c, Quantification of the spatial sensitivity maps\nshown in b. Euclidean distance was the summed absolute difference between early and all FAs. (n = 8, *\nis p < 0.05)\nRev2.2) The visual stimuli were lateralized in the task. Was the involvement of 5-HT in attentional\nmodulation lateralized? Photometry and optogenetic stimulation in DR in the study appear to\ncover both hemispheres, however, 5-HT wide-field imaging in V1 was done unilaterally. Does the\npredictiveness of V1 5-HT in attention performance depend on laterality of the cue?\nThe reviewer asks if 5HT release is lateralized during our task. This issue is also included in our\nresponses to Reviewer 1. To investigate lateralization, we performed new experiments where we\nused standard methods to virally express the genetically encoded 5HT sensor (GRAB5HT) across\nthe entire mouse brain and then widefield imaged GRAB5HT signals from the entire dorsal cortex\nwhile mice attended and detected visual stimuli.\nWe first compared GRAB5HT dynamics in V1 before grating onset on cued-left and cued-right hit\ntrials. This analysis shows that both V1s, regardless of cue location, experience the rapid,\ntransient drop in 5HT that we causally linked to enhanced behavioral attention gain (Reviewer\nFig. 1b-e). This preliminary result also suggests a potential neural mechanism for how changes\nin 5HT release might regulate attentional gain – by gating or modulating ongoing attentional\ncomputations that are already lateralized.\nThe circuitry behind attentional computations is poorly understood, but some work suggests that\nfeedback inputs from midline frontal cortical areas (such as the anterior cingulate cortex) could\nplay an important role14,26,61. Considering these studies, it is interesting that both V1 and cingulate\nshow similar dynamics before hits (Reviewer Figs. 1d, 4b) and false alarms (Reviewer Fig. 4c).\nIt is also worth noting that 5HT release drops in a similar way in both V1s prior to hits (Reviewer\nFig. 1c). More work will be needed to see if 5HT dynamics strengthen a lateralized ACC-V1\nfeedback during our task and whether such potential effects are related to visual attention.\nRev2.3) The authors appear to fixate that 5-HT modulation on visual attention is through V1.\nIndeed, authors found 5-HT dynamics in V1 resembled that of DR photometry recordings.\nHowever, such correlation was not causal. Optogenetic stimulation of DR can affect 5HT release\nin many other downstream targets. Besides V1, DR-5HT neurons also densely project to superior\ncolliculus (SC) and exert potent suppression of SC visually evoked activity. Given SC is involved\nin visual attention, including in mice, it is plausible that 5-HT release in SC is playing an even\nmore prominent role in attention. Ideally, optogenetic manipulation of 5-HT axon terminals in V1\ncould provide a definitive answer. The authors should at least discuss 5-HT release in other visual\nareas in attentional control.\nWe thank the reviewer for this important point and regret that our writing emphasized the idea\nthat V1 is the sole site of action for 5HT during our task. Indeed, preliminary widefield imaging\nthat we performed for this rebuttal suggest that the 5HT signals are present in both posterior visual\n11\nareas and midline frontal cortical areas (Reviewer Figs 1d, 4b,c). Optical stimulation of DR-5HT\nterminals is tricky since excitation could spread from the stimulated region into collaterals\ninnervating other brain regions. We think a better experiment could be suppression of DR-5HT\nterminals in V1 and are in the process of validating various suppression tools including new opsins\nthat specifically suppress neurotransmitter release62. Our revised manuscript now contains an\nexpanded discussion of other cortical locations and the possibility of parallel action of 5HT.\nResponse to reviewer 3\nRev3.1) In the study “Rapid dynamics of dorsal raphe serotonin neurons selectively regulate the\ngain of visual attention” by Lehnert and colleagues, the authors show that fast dynamics of\nserotonin in dorsal raphe and V1 set the attentional gain, independent of location (using calcium\nimaging in DR, serotonin sensors in V1, and optogenetics). The paper is well written, the results\nare clear, and the conclusions are very important and well supported by the data. The discussion\ndoes a good job at linking these results with the broader literature on the topic.\nWe thank the reviewer for these very kind words and are delighted that they enjoyed the paper,\nfelt the results were clear, and thought the conclusions important.\nRev3.2) I only have some minor points to raise, primarily for clarification purposes:\nP2: “Gratings switched sides after ~35 trials and had a flat probability of appearance in time (Fig.\n1b).” Fig 1b does not show that gratings switched nor the flat probability (I think this figure\nreference should be moved).\nCorrected.\nRev3.3) What is the proportion of trials with FAs and misses?\nFAs during the delay period do not terminate a trial in our task. Instead, such FAs just prolong the\ntrial, until the delay period is lick-free; these FAs are used to compute behavioral attention\nmeasures. If the delay is lick-free, then a coherent grating is shown and a mouse can either hit or\nmiss. Thus, the proportion of trials ending in a miss can be calculated from our psychometric\ncurve of hits [% of misses = 100 - % of hits], which varies based on grating coherence.\nWhen the three-bar grating finally appeared, approximately 14% of the time the grating coherence\nwas zero and thus not visible. Because these other FAs occurred during the reaction time window,\nthey allow us to estimate the proportion of hits that were due by chance, which is used for our d-\nprime calculations.\nRev3.4) Is the quantification in Figure 1g in the central bin only? Or average across bins? (Same\nfor Figure 3i and 5k).\nOur model fits spatial sensitivities and behavioral gain to cued and uncued checkerboards\nseparately. The quantification in Manuscript Fig. 1g and elsewhere is the attentional index based\non cued and uncued behavioral gain (model  and  ). The spatial organization of the\ncued uncued\nsensitivity maps () are independent of  and not used in this measure. In Manuscript Fig. 1e,\nwe show how sensitivity maps are scaled by behavioral gain.\nRev3.5) “These results reveal that a second-scale elevation in DR activity precedes a miss,\nwhereas a rapid suppression precedes a hit.” Why do they show that there is an elevation? With\nrespect to hits? Or with respect to baseline? If the former, then this should be rephrased to reflect\nwhat the data shows\nIt was indeed with respect to hits. We have rephrased this sentence.\nthan changes to impulsivity, engagement, or strategy” Can you clarify which results show which\nof these claims? Are RT and lapse rate associated with engagement? Response bias to strategy,\nand criterion to impulsivity?\nWe have clarified the use of these terms in the manuscript. We associate sensitivity to gratings\nwith d-prime, hits, and threshold; impulsivity is measured as response bias; engagement is\nmeasured as lapse rate; and strategy measured as criterion.\nRev3.7) “Interestingly, sensitivity to vertical energy and local contrast remained at the center of\nthe screen in both DR high and low (Fig. 3h). This result suggested that rapid DR low dynamics\nare correlated with increases in attentional gain rather than changes in the way sensitivity was\nallocated across the screen.” There are no stats to support this claim. Is the center-bias in DR\nhigh significant? (same applies to figure 4h and 4m)\nThe reviewer asks if the spatial sensitivities to vertical and contrast energy ( and  ) are indeed\nv c\nthe same between DR high and low, and between LED ON, LED off, and Ctrl. In Reviewer Fig.\n9 we compare model spatial sensitivity to vertical and contrast energy at the central binned\nchecker to that of the surrounding bins for our photometry experiments (Manuscript Fig. 3). This\ncomparison shows that model sensitivity remains centered, regardless of our photometry\nmeasures of DR-5HT activity (Reviewer Fig. 9a-b).\nReviewer Figure 9.a, Average model sensitivity maps for vertical and contrast energy computed from false\nalarms (FAs) preceded by high or low DR-5HT activity. b, Vertical and contrast sensitivities averaged for\nthe center and all other surrounding checker bins for 8 individual mice during DR-5HT low and DR-5HT\nhigh. Sensitivity remains at the central bin regardless of DR-5HT activity. (n = 8 mice, ** is p < 0.01; *** is\np < 0.001)\nWe also extended this analysis to model sensitivities obtained with optogenetic and GRAB5HT\nexperiments. Model sensitivities remained centered in LED ON, LED OFF, ctrl, GRAB5HT high,\nand GRAB5HT low conditions. All these new analyses are integrated into the extended data\nfigures of the revised manuscript and strengthen our claim that DR-5HT activity regulates the\nbehavioral gain of visual attention rather than its spatial sensitivity (focus).\nRev3.8) Can you add some discussion to understand diffused effects of serotonin on the weighted\nsensitivity maps of 5i-j?\nThe reviewer wonders why the vertical energy attention maps for the GRAB5HT cohort appear\nmore diffused than that of the photometry and optogenetic cohorts.\nBriefly, it has to do with the nature of our task. We previously showed that mice monitor 3 main\nfeatures when detecting the coherent 3-bar grating: Spatial frequency around 0.07 cycles per\ndegree, vertical energy, and local contrast energy59. In that study, we noticed variability in what\nmice chose to monitor. Many choose to monitor all 3 features, but some tend to emphasize one\nfeature over another. Such biased mice often choose local contrast or vertical energy.\ncontained some of these contrast-biased mice; the example mouse in Manuscript Fig 5i uses\nboth. This is why the average vertical energy maps (Manuscript Fig 5j) appear more diffused\nwhereas the contrast energy maps are more defined. We now include some discussion on this\npoint and directly quantify the centered-ness of the sensitivity maps for GRAB5HT, photometry,\nand optogenetic cohorts through statistical comparisons located in the Extended Data Figures\n4-6. New panels in Extended Data Fig. 6c show that 3 to 4 mice chose contrast over vertical\nenergy which is why the average looks more diffuse for this feature.\nResponse to reviewer 4\nRev4.1) In this manuscript, Lehnert et al. show that 5-HT modulates visual attention, with\ndecreases in 5-HT neural activity and release in V1 resulting in greater attention towards visual\ncues. The authors employ a useful\nbehavioral paradigm for tracking attention\nin addition to fiber photometry,\noptogenetics and widefield imaging to\ntrack and manipulate DR neurons/5-HT\nrelease during execution of this task.\nConsistent findings across these\ntechniques are a strength of the\nmanuscript. The data could be more\nclearly presented and described.\nWe thank the reviewer for their time in\nreading the manuscript. In response to\nthis and the other reviewers, we have\nmade numerous changes to improve the\nclarity of the manuscript.\nRev4.2) Showing fluorescence data\nwithout filters applied would be a useful\nsupp figure given the authors noted caveat\nthe filters can change activity around large\ndeviations (even if the authors removed\nthe Hit activity prior to the filter, it would be\ngood to show less processed data that\nshow this to be the case)\nReviewer Figure 10. a, Example session fluorescence\nsignal evoked by 470 nm and 405 nm LEDs during\nWe thank the reviewer for this suggestion.\nphotometry. b, The same 470 nm signal as in panel a\nIn Reviewer Fig. 10, we show our raw 470\nfollowing regression to the 405 nm signal. c, a high-pass\nnm and 405 nm fluorescent signals from filtered version of the signal in b. d, magnified view of the\nan entire session of one example mouse, blue boxed region of the unfiltered and filtered signals in\nthe unfiltered signal following 405 c. e, example photometry signals taken from the indicated\nprocessing stages illustrated in a-d.\ncorrection, the same signal following high-\npass filtering, and the DR-5HT signal obtained from the various pre-processing steps. We now\ninclude these panels in Extended Data Fig. 1 in the revised manuscript.\nRev4.3) Are there differences in behavior and/or 5-HT activity in trials that follow a FA vs trial start\nlick?\nIn trained mice, all trials start with a lick (start lick, Manuscript Fig. 2f) and are followed by a\ndelay period. We have very few delay periods that are false-alarm free. Most mice false alarmed\nto noise, which we previously explained as occurring because the noise patterns happen to look\nlike the stimulus59. Most of our FAs (>90%) follow other FAs rather than following a start lick. Our\nlabel “start or FA licks” in Manuscript Figs. 2g&5d may have misled the reviewer. We have\nrevised these labels to “FA licks”.\nWe used the y-scaling in order to make it easier to see our differences in d-prime and threshold.\nThe same is true for reaction time which begins at 0.4 and ends at 0.7. We respectfully feel this\nis the best way to report our differences which are significant for d-prime and threshold, and so\nhave left these axes labels the same. We can change this, however, if the reviewer and editor feel\nstrongly about this issue.\nMinor comments:\nRev4.5) -5HT recording data at the level of V1 does not correlate as robustly with attention as\nphotometry data, and gain/loss of function data is at the level of the DR. This limits the ability to\nstate that the 5-HT modulation is at the level of V1 (or at least only at the level of V1).\nThe reviewer points out that V1 may not be the only site at which rapid DR-5HT low signals act\nto regulate the gain of visual attention. We agree, and based on comments from Reviewers 1&2,\nwe have now softened this emphasis on V1 as the sole site of 5HT action throughout the\nmanuscript and have included a new discussion paragraph that summarizes all candidate cortical\nareas.\nRev4.6) -In some supp figure legends delta f/F is written as delta f/Ff\nCorrected.\n-Colors in supplemental figures often re-use blue/orange color scheme despite not referring to\nthe same features as the main figures (e.g., Figure S3 uses blue/orange for hit/miss and has\nentirely new colors for high/low DR activity). Figures should have consistent themes to help\nreadability.\nWe have changed the color scheme of hits and misses in Extended Data Figure 3 in the revised\nmanuscript. We retain the green/purple DR high and low in this figure to distinguish these “slow”\nDR high and low states from the fast ones in Manuscript Figs 2,3,5 that we have associated with\nthe gain of visual attention.\nReferences\n1. Luo, M., Li, Y. & Zhong, W. Do dorsal raphe 5-HT neurons encode ‘beneficialness’?\nNeurobiol Learn Mem 135, 40–49 (2016).\n2. Cohen, J. Y., Amoroso, M. W. & Uchida, N. Serotonergic neurons signal reward and\npunishment on multiple timescales. Elife 4, (2015).\n3. Ren, J. et al. Anatomically Defined and Functionally Distinct Dorsal Raphe Serotonin Sub-\nsystems. Cell 175, 472-487.e20 (2018).\n4. Miyazaki, K. W. et al. Optogenetic activation of dorsal raphe serotonin neurons enhances\npatience for future rewards. Current biology: CB 24, 2033–2040 (2014).\n15\n5. Grossman, C. D. & Cohen, J. Y. Neuromodulation and Neurophysiology on the Timescale of\nLearning and Decision-Making. Annu Rev Neurosci 45, 317–337 (2022).\n6. Grossman, C. D., Bari, B. A. & Cohen, J. Y. Serotonin neurons modulate learning rate\nthrough uncertainty. Current Biology 32, 586-599.e7 (2022).\n7. Muzerelle, A., Scotto-Lomassese, S., Bernard, J. F., Soiza-Reilly, M. & Gaspar, P.\nConditional anterograde tracing reveals distinct targeting of individual serotonin cell groups\n(B5-B9) to the forebrain and brainstem. Brain Struct Funct 221, 535–561 (2016).\n8. Chebolu, S., Dayan, P. & Lloyd, K. Vigilance, arousal, and acetylcholine: Optimal control of\nattention in a simple detection task. PLoS Comput Biol 18, e1010642 (2022).\n9. Gritton, H. J. et al. Cortical cholinergic signaling controls the detection of cues. Proc Natl\nAcad Sci U S A 113, E1089-1097 (2016).\n10. Pinto, L. et al. Fast modulation of visual perception by basal forebrain cholinergic neurons.\nNat Neurosci 16, 1857–1863 (2013).\n11. Sarter, M. & Lustig, C. Cholinergic double duty: cue detection and attentional control. Curr\nOpin Psychol 29, 102–107 (2019).\n12. Parikh, V. & Bangasser, D. A. Cholinergic Signaling Dynamics and Cognitive Control of\nAttention. Curr Top Behav Neurosci 45, 71–87 (2020).\n13. Noudoost, B. & Moore, T. The role of neuromodulators in selective attention. Trends Cogn\nSci 15, 585–591 (2011).\n14. Huda, R. et al. Distinct prefrontal top-down circuits differentially modulate sensorimotor\nbehavior. Nat Commun 11, 6007 (2020).\n15. Okaty, B. W. et al. A single-cell transcriptomic and anatomic atlas of mouse dorsal raphe\nPet1 neurons. Elife 9, e55523 (2020).\n16. Muzerelle, A., Scotto-Lomassese, S., Bernard, J. F., Soiza-Reilly, M. & Gaspar, P.\nConditional anterograde tracing reveals distinct targeting of individual serotonin cell groups\n(B5-B9) to the forebrain and brainstem. Brain Struct Funct 221, 535–561 (2016).\n17. Weber, E. T. & Andrade, R. Htr2a Gene and 5-HT(2A) Receptor Expression in the Cerebral\nCortex Studied Using Genetically Modified Mice. Front Neurosci 4, 36 (2010).\n16\n18. Okaty, B. W., Commons, K. G. & Dymecki, S. M. Embracing diversity in the 5-HT neuronal\nsystem. Nat Rev Neurosci 20, 397–424 (2019).\n19. Lambe, E. K., Fillman, S. G., Webster, M. J. & Shannon Weickert, C. Serotonin receptor\nexpression in human prefrontal cortex: balancing excitation and inhibition and excitation across postnatal\ndevelopment. PLoS One 6, e22799 (2011).\n20. Salvan, P. et al. Serotonin regulation of behavior via large-scale neuromodulation of\nserotonin receptor networks. Nat Neurosci 26, 53–63 (2023).\n21. Berger, M., Gray, J. A. & Roth, B. L. The expanded biology of serotonin. Annu Rev Med 60,\n355–366 (2009).\n22. Couto, J. et al. Chronic, cortex-wide imaging of specific cell populations during behavior. Nat\nProtoc 16, 3241–3263 (2021).\n23. Chan, K. Y. et al. Engineered AAVs for efficient noninvasive gene delivery to the central and\nperipheral nervous systems. Nat Neurosci 20, 1172–1179 (2017).\n24. Wan, J. et al. A genetically encoded sensor for measuring serotonin dynamics. Nat Neurosci\n24, 746–752 (2021).\n25. Feng, J. et al. Monitoring norepinephrine release in vivo using next-generation GRAB\nNE\nsensors. Preprint at https://doi.org/10.1101/2023.06.22.546075 (2023).\n26. Zhang, S. et al. Selective attention. Long-range and local circuits for top-down modulation of\nvisual cortex processing. Science 345, 660–665 (2014).\n27. Huang, K. W. et al. Molecular and anatomical organization of the dorsal raphe nucleus. Elife\n8, e46464 (2019).\n28. Cho, J. R. et al. Dorsal Raphe Dopamine Neurons Signal Motivational Salience Dependent\non Internal State, Expectation, and Behavioral Context. J Neurosci 41, 2645–2655 (2021).\n29. Li, Y. et al. Serotonin neurons in the dorsal raphe nucleus encode reward signals. Nat\nCommun 7, 10503 (2016).\n30. Hong, S. Z. et al. Norepinephrine potentiates and serotonin depresses visual cortical\nresponses by transforming eligibility traces. Nat Commun 13, 3202 (2022).\n17\n31. Azimi, Z. et al. Separable gain control of ongoing and evoked activity in the visual cortex by\nserotonergic input. Elife 9, e53552 (2020).\n32. Seillier, L. et al. Serotonin Decreases the Gain of Visual Responses in Awake Macaque V1.\nJ Neurosci 37, 11390–11405 (2017).\n33. Patel, A. M., Kawaguchi, K., Seillier, L. & Nienborg, H. Serotonergic modulation of local\nnetwork processing in V1 mirrors previously reported signatures of local network modulation\nby spatial attention. Eur J Neurosci 57, 1368–1382 (2023).\n34. Barzan, R. et al. Gain control of sensory input across polysynaptic circuitries in mouse\nvisual cortex by a single G protein-coupled receptor type (5-HT2A). Nat Commun 15, 8078\n(2024).\n35. Reggiani, J. D. S. et al. Brainstem serotonin neurons selectively gate retinal information flow\nto thalamus. Neuron 111, 711-726.e11 (2023).\n36. Chen, C. & Regehr, W. G. Presynaptic modulation of the retinogeniculate synapse. J\nNeurosci 23, 3130–3135 (2003).\n37. Seeburg, D. P., Liu, X. & Chen, C. Frequency-dependent modulation of retinogeniculate\ntransmission by serotonin. J Neurosci 24, 10950–10962 (2004).\n38. Sherman, S. M. & Guillery, R. W. Functional Connections of Cortical Areas : A New View\nfrom the Thalamus. (The MIT Press, London, England ; Cambridge, Mass., 2013).\n39. Kayama, Y., Shimada, S., Hishikawa, Y. & Ogawa, T. Effects of stimulating the dorsal raphe\nnucleus of the rat on neuronal activity in the dorsal lateral geniculate nucleus. Brain Res\n489, 1–11 (1989).\n40. Michaiel, A. M., Parker, P. R. L. & Niell, C. M. A Hallucinogenic Serotonin-2A Receptor\nAgonist Reduces Visual Response Gain and Alters Temporal Dynamics in Mouse V1. Cell\nRep 26, 3475-3483.e4 (2019).\n41. Syeda, A. et al. Facemap: A Framework for Modeling Neural Activity Based on Orofacial\nTracking. http://biorxiv.org/lookup/doi/10.1101/2022.11.03.515121 (2022)\ndoi:10.1101/2022.11.03.515121.\n18\n42. Carrasco, M., Penpeci-Talgar, C. & Eckstein, M. Spatial covert attention increases contrast\nsensitivity across the CSF: support for signal enhancement. Vision Res 40, 1203–1215\n(2000).\n43. Fernández, A., Okun, S. & Carrasco, M. Differential Effects of Endogenous and Exogenous\nAttention on Sensory Tuning. J Neurosci 42, 1316–1327 (2022).\n44. Giordano, A. M., McElree, B. & Carrasco, M. On the automaticity and flexibility of covert\nattention: a speed-accuracy trade-off analysis. J Vis 9, 30.1–10 (2009).\n45. Kirsch, W., Heitling, B. & Kunde, W. Changes in the size of attentional focus modulate the\n modulate the\napparent object’s size. Vision Research 153, 82–90 (2018).\n46. Liu, T. & Mance, I. Constant spread of feature-based attention across the visual field. Vision\nResearch 51, 26–33 (2011).\n47. Müller, M. M. et al. Feature-selective attention enhances color signals in early visual areas\nof the human brain. Proc. Natl. Acad. Sci. U.S.A. 103, 14250–14254 (2006).\n48. Mitchell, K. J. The genetics of neurodevelopmental disease. Curr Opin Neurobiol 21, 197–\n203 (2011).\n49. Cohen, M. R. & Maunsell, J. H. R. Attention improves performance primarily by reducing\ninterneuronal correlations. Nat Neurosci 12, 1594–1600 (2009).\n50. Ruff, D. A. & Cohen, M. R. Simultaneous multi-area recordings suggest that attention\nimproves performance by reshaping stimulus representations. Nat Neurosci 22, 1669–1676\n(2019).\n51. Ruff, D. A., Xue, C., Kramer, L. E., Baqai, F. & Cohen, M. R. Low rank mechanisms\nunderlying flexible visual representations. Proc Natl Acad Sci U S A 117, 29321–29329\n(2020).\n52. Cook, E. P. & Maunsell, J. H. R. Attentional modulation of motion integration of individual\nneurons in the middle temporal visual area. J Neurosci 24, 7964–7977 (2004).\n53. Cohen, M. R. & Maunsell, J. H. R. A neuronal population measure of attention predicts\nbehavioral performance on individual trials. J Neurosci 30, 15241–15253 (2010).\n19\n54. Speed, A., Del Rosario, J., Mikail, N. & Haider, B. Spatial attention enhances network,\ncellular and subthreshold responses in mouse visual cortex. Nat Commun 11, 505 (2020).\n55. Kanamori, T. & Mrsic-Flogel, T. D. Independent response modulation of visual cortical\nneurons by attentional and behavioral states. Neuron 110, 3907-3918.e6 (2022).\n56. McBride, E. G., Lee, S.-Y. J. & Callaway, E. M. Local and Global Influences of Visual Spatial\nSelection and Locomotion in Mouse Primary Visual Cortex. Curr Biol 29, 1592-1605.e5\n(2019).\n57. Myers-Joseph, D., Wilmes, K. A., Fernandez-Otero, M., Clopath, C. & Khan, A. G.\nDisinhibition by VIP interneurons is orthogonal to cross-modal attentional modulation in\nprimary visual cortex. Neuron 112, 628-645.e7 (2024).\n58. Poort, J. et al. Learning and attention increase visual response selectivity through distinct\nmechanisms. Neuron 110, 686-697.e6 (2022).\n59. Lehnert, J. et al. Visual attention to features and space in mice using reverse correlation.\nCurr Biol 33, 3690-3701.e4 (2023).\n60. Speed, A. & Haider, B. Probing mechanisms of visual spatial attention in mice. Trends\nNeurosci 44, 822–836 (2021).\n61. Zhang, S. et al. Long-range and local circuits for top-down modulation of visual cortex\nprocessing. Science 345, 660–665 (2014).\n62. Mahn, M. et al. High-efficiency optogenetic silencing with soma-targeted anion-conducting\nchannelrhodopsins. Nat Commun 9, 4125 (2018).\nbeen added to the manuscript.\nReviewer #1 (Remarks to the Author): I am grateful to the authors for their detailed and thoughtful\nreplies to my initial questions. Despite these clarifications, I still find it difficult to fully grasp the impact\nand broader meaning of the results presented in this paper. My second round of questions is therefore\naimed at achieving a more precise understanding.\nWe thank the reviewer for acknowledging our prior revisions. The reviewer requests a more precise\nunderstanding of the meaning and impact of our study.\nMeaning: Our central finding is that rapid DR-5HT dynamics specifically regulates how strongly a mouse\nattends to task-relevant features on the cued screen versus those on the uncued screen. We used a\nbehavioral method (Manuscript Fig. 1) to measure this quantity (hereafter termed attentional strength)\nand identify where on the screen they attended for task-relevant features (hereafter termed attentional\nfocus). We saw that rapid endogenous and optogenetically-evoked DR-5HT dynamics selectively\nregulated attentional strength but did not affect attentional focus or measures of states (eg: impulsivity,\narousal) unrelated to the stimulus. This result suggests that (1) the brain controls attentional strength and\nfocus separately; and (2) that rapid DR-5HT neuromodulation regulates attentional strength.\nImpact: These findings are impactful for several reasons: (1) It is the first demonstration that DR and\n5HT regulate visual attention; (2) It is the first demonstration that DR-5HT specifically controls attentional\nstrength; (3) It reveals that attentional strength and focus are computationally separable phenomena; and\n(4) It is the first report of perceptually-relevant, second-scale dynamics in DR activity and 5HT release.\nWhile these points alone make our study impactful to neuroscientists interested in visual circuits,\n5HT\nbiology, and neuromodulation, we now also formalize our results in a computational model which explains\nour principal findings using the canonical cortical computation of normalization. This new aspect will also\nmake our study impactful to theorists and experimentalists working on cortical computation.\nRev1a Scope of the main findings\nThe primary result of this paper is not a modulation of visual processing by serotonin within visual cortex\n(as I originally suspected in Question 1), but rather a more general effect of serotonin on visual detection\nbehavior.\nThe reviewer is correct that our paper is not about 5HT modulation of visual cortical (V1) neurons.\nHowever, we respectfully point out that this was not a claim we made nor was it a central goal of our\nrevision or original submission.\nMoreover, we respectfully disagree with the reviewer’s statement that our findings show an unspecific\ngeneral effect of serotonin on visual detection behavior. Had DR-5HT non-specifically modulated\nsensitivity to stimulus energy (ie: a general effect on visual detection) it would have impacted both cued\nand uncued sides equally and would not change attentional strength (since it reflects cued-uncued\ndifference). Instead, our studies show that rapid DR-5HT dynamics specifically regulate attentional\nstrength. We arrive at this conclusion because:\n1. DR-5HT fluctuations before false alarms (FAs) predicted attentional strength: Low DR-5HT predicted\nstrong attention whereas high DR-5HT predicted weak or no attention (Manuscript Fig. 3,5). We\nmeasured attentional strength and focus from FAs (Manuscript Fig. 1)\n2. Rapid DR-5HT dynamics also predicted grating detection (performance) which was likely due to its\neffects on attentional strength (Manuscript Fig. 2-5).\n3. DR-5HT fluctuations before FAs did not affect the locations within a screen a mouse chose to attend\n(attentional focus), nor did they affect FA rate (Manuscript Ext. Data Fig. 4,5)\n4. DR-5HT fluctuations before grating onset did not affect guess rate (impulsivity), lapse rate\n(motivation), or criterion (strategy) (Manuscript Fig. 3-5).\n5. Only rapid DR-5HT fluctuations predict changes in attentional strength (Manuscript Ext. Data Fig.\n3); we see slow fluctuations, like others do, but they were unrelated to our behavioral measures.\n6. Optogenetic DR excitation before FAs reduced the attentional strength to zero (Manuscript Fig. 4,\n1\nalso see Rev.2 below); excitation before grating onset reduced performance, likely due to low\nattentional strength.\n7. Optogenetic DR suppression before FAs increased attention strength (Manuscript Fig. 4);\nsuppression before grating onset improved performance, likely due to high attentional strength.\n8. Neither optogenetic perturbation changed our measures of attentional focus, impulsivity, motivation,\nor strategy (Manuscript Fig. 4, Ext. Data Fig. 5).\nWe have revised text throughout the introduction and results to make this clearer. We have also\nsubstantially revised our discussion using a computational model (see below).\nRev1b The authors’ data do not support any claim of a gain change in V1, even if such a mechanism\nremains possible. Indeed, as they note in the first paragraph of the discussion section (“How does DR-\n5HT contribute to attentional network”), serotonin’s action on V1 networks could contribute to attentional\ngain modulation. Given that serotonin is generally believed to suppress cortical excitatory neurons, a\ndecrease in serotonin release within V1 would be expected to increase neuronal excitability.\nThe reviewer is correct that our data does not support any claim of gain change in V1. However, as we\nmention above, this was not a claim we made. Our past Manuscript Discussion used prior work to\nspeculate that 5HT’s suppression of visual neurons could be how it regulates attentional strength. Here\nwe address this prediction and others by formalizing our results in a computational model that captures\nour key findings: that non-lateralized DR-5HT specifically and inversely regulates attentional strength.\nWe obtained and adapted the code for the highly-cited and influential normalization model of attention1\n(Rev. Fig. 1a). We chose this particular model because normalization is a canonical neural computation2,\na well-accepted feature of visual cortical circuits1, and because this model accounts for many observed\nbehavioral and neural attentional signatures (eg: contrast/response/feature-similarity gain, biased\ncompetition)1–5.\nBriefly, left and right stimuli drive brain representations that are multiplied by internal attention fields for\ncued and uncued sides. In this way, attention fields selectively boost one representation without affecting\nthe other (Rev. Fig.1a). Cued and uncued representations are then pooled over features and space to\ncreate suppressive drive. Finally, cued and uncued representations are divided by the suppressive drive\n(normalized) to produce a stronger population response on the cued side (right stimulus is cued). Since\nprior work showed this neural population model also explains behavioral effects of attention1–5, we\ninterpret differences in right versus left model population responses (model attentional strength) as\nproducing our behavioral measure of attentional strength.\nWe hypothesized that DR-5HT scaled the suppressive drive, a key computation regulating model\nattentional strength. To test this, we multiplied the suppressive drive by a single parameter reflecting DR-\n5HT and observed the model’s predictions (Rev. Fig.1b-c).\nModel output aligned exactly with our observations. Model population responses were stronger on the\ncued side than the uncued side when DR-5HT was low (Rev. Fig.1b). This is because as the denominator\n(suppressive drive) shrinks, the attention field’s effects on model output grow. Interestingly, cued and\nuncued population responses were roughly equal when DR-5HT is high (Rev. Fig.1c). This is because\nas the denominator (suppressive drive) grows, the attention field’s effects shrink. Computing an index\nfrom the maximum population response to cued and uncued confirms these observations and shows\nhigher model attention index when DR-5HT is low versus when it is high (Rev. Fig.1d). Note that changes\nin DR-5HT do not affect the attention field itself (model attentional focus), rather DR-5HT only scales the\nattention field’s effect on population responses (model attentional strength). Thus, our normalization\nmodel suggests a computational mechanism for separating attentional strength and focus (Rev. Fig.1e).\nIt is important to point out that the model could have failed to capture our results, regardless of where we\nput DR-5HT in its architecture. However, DR-5HT specifically mimicked our results when it scaled the\nsuppressive drive. Since our model inherits the original model’s biologically-rooted architecture (eg:\nstimulus representations compete, top-down attention field, suppressive normalization, etc), it predicts\nthat 5HT should suppress cortical neurons holding representations of attention targets. This prediction is\nmodel of attention from Reynolds and Heeger 2009. Stimulus drive represents the neural population response\nto left and right checkerboard stimuli if there were no attention or normalization. Stimulus drive is depicted with\nreceptive field location along the horizontal and feature preference (orientation, etc) along the vertical. The\nattention field depicts the effect of cueing the visual location of the right stimulus but is assumed to be non-\nspecific to visual features. The attention field multiplies the stimulus drive, and its product is pooled over space\nand features to generate the suppressive drive. The panel on the right shows the population response (R) of\nneurons produced by dividing the product of stimulus drive and attention field by the suppressive\ndrive. b, Modified normalization model includes a term to scale the suppressive field by DR-5HT (DR-5HT\ndrive). Low DR-5HT strengthens attention to the cued side. c, Same as b, but for DR-5HT high which weakens\nattention to the cued side. Maximum population response to cued and uncued indicated (Rmax and\ncued\nRmax ). d, Top: equation to computed model attention index from Rmax and Rmax ; Bottom: Model\nuncued cued uncued\nattention index computed from population responses shown in b (low DR-5HT drive) and c (high DR-5HT\ndrive). Higher model attention index when DR-5HT is low versus when it is high mimics our behavioral results\nshowing increased attentional strength. e, Cartoon of modified normalization model with attentional focus and\nattentional strength indicated. The attention field focuses the circuit on target features and DR-5HT signals\nscale suppressive drive to regulate how strongly such targets are amplified in the population responses that\nunderlie behavioral attention.\nsupported by reports of excitatory 5HT receptors expression in cortical interneurons6–10, inhibitory 5HT\nreceptors expression in pyramidal neurons11–13, and optogenetic evidence showing suppression of V1\npyramidal neurons by DR axon stimulation14,15.\nThe model also shows how non-lateralized 5HT produces lateralized enhancement of cued output (see\nour response to Rev. 2 below) and explains how DR-5HT inversely controls attentional strength (see our\nresponse to Rev. 3c below). Since our modified normalization model provides a useful scope for our\nresults and discussion, we now include it as Manuscript Fig 6 and describe it in the methods section.\nWe also further de-emphasize 5HT’s effects in V1 in our introduction but retain it in the discussion.\nRev.2. On lateralization\nMy second concern stemmed from the apparent lateralization of attention to the visual stimulus, which\ninitially raised the possibility that serotonin might contribute to lateralized visual processing. However, the\nauthors’ preliminary results do not support this view: no evidence of lateralization in serotonin-related\nactivity was observed. The source of this lateralization therefore remains unknown and is unlikely to be\ndirectly explained by lateralized serotonergic modulation. As the authors themselves note in their reply to\nQuestion 2, serotonin is more likely involved in setting a general attentive brain state, albeit in a negative\nfashion (see comment 3 below). In this context, the observation that excitation of DR-5HT neurons is\nassociated with a switch of attention to the unattended side (Fig. 4i) is difficult to reconcile with their\ninterpretation. This finding is neither highlighted in the results section nor addressed in the discussion.\nThere are 3 points here, the reviewer: 1) suggests a non-lateralized 5HT signal cannot produce lateralized\nattentional effects; 2) suggests that Manuscript Fig. 4i supports “a switch of attention to the unattended\nside”; 3) suggests our 5HT results set a “general attentive brain state”.\n1. Enhanced attention to the cued side can arise from non-lateralized DR-5HT signals if 5HT\nregulates normalization. The reviewer hypothesizes that only a lateralized 5HT signal could underlie\nlateralized attention to the cued side and states that our preliminary results do not support their\nhypothesis. However, we hypothesized in our past response that DR sends a global (ie: non-lateralized)\nsignal to enhance ongoing lateralized mechanisms that embody attentional focus.\nOur modified normalization model, which uses a non-lateralized (ie: global) multiplicative term for DR-\n5HT, refines the hypothesis in our prior response: non-lateralized DR-5HT regulates the suppressive field,\nand through normalization, scales the effect of the attention field on the population response (Rev. Fig.\n1b-d).Thus, DR-5HT regulates how strongly the model attends the cued side but does not affect what\nattention focuses upon (Rev. Fig. 1e). These model results are remarkably similar to our main findings.\n2. We interpret Fig 4i as “no attention” rather than a switch to the uncued side: We thank the\nreviewer for pointing this out to us and we can see how it may appear to indicate a switch of attention to\nthe unattended side because we did not include statistics to show otherwise. However, the effect across\nall mice is not significantly different from zero (ie: not attending to either side) and was influenced by two\nmice that happened to show unusually strong uncued model gain. The collective results shown in\nManuscript Figs. 1g, 3i, 4n, 5k, and Extended Fig. 4c, are consistent with each other and support our\nclaim that when DR-5HT is high, attention to the cued screen weakens. For these reasons, we interpret\npanel 4i as “no attention” rather than a switch to the uncued side.\nWe now indicate that this point is not significantly different from zero in the figure legend and results. We\nalso discuss how high DR-5HT leads to “weak attention” using our modified normalization model in the\nrevised discussion.\n3. DR-5HT does not set “a general attentive brain state”, it regulates the strength of attention. We\nrespectfully direct the reviewer to our response to this point above in Rev.1a.\nRev.3a. Interpretation of DR-5HT suppression\n….The authors present the suppression of DR-5HT activity during successful sensory processing as\nevidence for an active involvement in attention.\nThe reviewer suggests that we used DR-5HT low dynamics “during” successful sensory processing (we\nassume this means DR-5HT when a mouse detects the grating or hits) as evidence that DR-5HT was\ninvolved in attention. We apologize that this was not clearer, but while the relationship between pre-\ngrating DR-5HT suppression and hits is consistent with attention, it is not our main evidence for this point.\nThe direct evidence comes from comparing behavioral attention measures between false alarm licks\npreceded by high versus low DR-5HT activity. We drew a causal (active) link between DR-5HT and\nattentional strength with optogenetics (Manuscript Fig. 4): When we optogenetically increased DR\nactivity before false alarms, we reduced attentional strength; optogenetically decreasing DR activity\nincreased attentional strength.\nRev.3b. Yet, DR-5HT neurons are less active during these hit trials. Their activity instead increases during\nreward processing.\nThe reviewer suggests that: (1) DR-5HT neurons are less active on hit trials; (2) the rapid suppression\nthat controls attention is actually due to post-grating reward transients.\n(1) Rapid DR-5HT dynamics fluctuate across both hit and miss trials as animals perform the task.\nAverage DR-5HT tends to be low just before grating onset on trials that mice correctly detect (Manuscript\nFig. 2h). During the trial itself, DR-5HT activity is not significantly different for hit and miss trials (Rev.\nFig. 2a-c). We think our poor labelling of Manuscript Fig. 2h could lead to the interpretation that it shows\nthe entire trial length when it does not. Our trial lengths are variable (secs to minutes), because delay\nperiod FAs seamlessly restart the cue. Both long and short trials can end in hit or miss, and all trials\nexhibit rapid DR-5HT dynamics throughout their entire length (Rev. Fig. 2a, new Manuscript Fig. 2f).\nManuscript Figure 2h Hit only shows the final portion of many hit trials sorted by delay length;\nthere\nwere many FAs and trial restarts before this final portion. Manuscript Figure 2i compares mean F/F\nfor this final part of hit and miss trials and shows this mean is lower for hits. We have clarified the labelling\nto indicate that only the last 15sec of the trial are shown in Manuscript Fig. 2h.\nWhen the entire pre-grating trial length is considered, signal distributions of DR-5HT for hits and misses\nare indistinguishable (an example mouse shown in Rev. Fig. 2b). Repeating this analysis across mice\nshowed no significant difference in the mean of such hit and miss distributions (Rev. Fig. 2c). This is\nconsistent with rapid DR-5HT dynamics which fluctuate many times over the length of a trial.\nAll the rapid DR-5HT dynamics we analyzed happened on timescales faster than our overall average trial\nlength of 34sec (Rev. Fig. 2d). On the other hand, slow dynamics did not impact behavior in our task\n(Manuscript Ext. Data Fig. 3). Example dynamics are shown in Reviewer Fig. 2a, where many periods\nof high, low, and baseline DR-5HT signals appear within a trial. Taken together, these results show that\nthe entire trial’s DR-5HT activity before grating onset is similar for hit and miss trials.\n(2) Pre-grating suppression does not depend on post-grating reward transient. We do not think the\npost-grating reward transient explains the rapid pre-grating dynamics because (1) DR-5HT rapidly\nfluctuates between the reward transient and the next pre-grating period (Rev. Fig. 2a); (2) We directly\nReviewer Figure 2. DR-5HT fluctuates rapidly over both hit and miss trials and is unrelated to the post-\ngrating response. a, Example trial licks (start, false alarm, detection, reward) and DR photometry labelled with\nperiods of high and low DR activity. DR activity fluctuates rapidly across the length of an entire trial. b,\nHistogram of average DR-5HT F/F across individual trials for 1 mouse sorted by hits and misses. c, Average\nDR-5HT F/F for hit and miss trials across 8 animals. DR-5HT signals across hit trials are statistically\nindistinguishable from controls and are close to baseline. d, power spectrum taken across an entire session of\nDR-5HT photometry processed as shown in Ext. Data Fig. 1. e, top: example photometry showing DR activity\nduring grating and during the subsequent trial’s pre-grating period. bottom: correlation between post-grating\nF/F in hit trials and the subsequent trial’s pre-grating F/F. No correlation was observed. f, top: example DR\nactivity during pre- and post-grating DR activity for one trial. Bottom: no correlation between pre- and post-grating\nDR activity trials with hit outcomes. Mice in e-f show individual differences in variation of post-grating F/F.\nsuppressed or elevated DR-5HT with optogenetics which significantly increased or decreased attention\nto the cued side.\nAs further proof, we saw no significant correlation between (1) the reward transient on hit trials and the\nnext trial’s pre-grating F/F (Rev. Fig. 2e); (2) the pre- and post-grating F/F for all hit trials (Rev. Fig.\n2f). This latter correlation is flat because it uses hits at all coherences (recall high coherence hits are not\npredicted by pre-grating DR-5HT) and because the trial-level reward transients are variable (Manuscript\nFig. 3b-c), the overall correlation is flat. Taken together, these new results and those in our manuscript\ndemonstrate that rapid DR-5HT fluctuations (that regulate attention) and post-grating transients (which\nprior work linked to reward) are separate phenomena. A reasonable hypothesis for the differing roles for\nthese two signals is that they come from different DR cell types whose singles are combined when\nreading from the optical fiber. We have integrated Reviewer Fig. 2e-f into Manuscript Extended Data\nFig. 2.\nRev.3c. A central aspect of the narrative strikes me as counterintuitive across the manuscript…..The\npaper frames this suppression as an “active implication,” which risks inverting the logic of interpretation.\nTo illustrate: it is as if a newspaper were to write, “Traffic accidents regulate improvements in city traffic,”\nwhen in reality accidents hinder traffic flow rather than drive its improvement.\nWe apologize but could not understand the reviewer’s analogy and hope the reviewer forgives any\nresulting misinterpretation of their request.\nWe think the reviewer is asking if (1) DR suppression or reward transient actively regulates visual\nattention and/or (2) whether inverted control of attention by DR-5HT is a reasonable biological\nmechanism.\n1) Rapid DR-5HT dynamics that control attention are separate from the post-grating reward\ntransient. It is possible the reviewer thinks that only post-grating reward-associated increase in DR-5HT\ncan be considered an active process and it is counterintuitive to think that DR-5HT fluctuations outside\nof this increase could be considered active. We strongly disagree with this premise.\nFirst, we show that the ongoing DR-5HT dynamics (high-low) are rapid and unrelated to the size of the\nreward transient (Rev. Fig. 2e-f). This means that DR-5HT dynamics (that regulate attention) are\nseparate from the post-grating responses (associated with reward). Second, the dynamic value of DR-\n5HT is causally linked to attentional strength –our rapid optogenetic manipulations (Manuscript Fig. 4)\nmimic our photometry and GRAB5HT results (Manuscript Fig.3,5). Given these points, we feel it is\nreasonable to conclude that rapid DR-5HT fluctuations actively regulate visual attention.\n(2) Inverse control is a canonical feature of many neural computations. Inverse relationships\ncharacterize many important brain signals. For example, the output neurons of the basal ganglia tonically\ninhibit thalamic neurons involved in movement; but become suppressed to release these thalamic\nneurons and cause movement. There are many other examples, including many stages of the visual\nsystem (eg: photons and photoreceptor potentials; OFF responses), or the many examples of\ndisinhibition which are cardinal features of many neural computations and circuits in the brain.\nOur modified normalization model also supports an inverse relationship between attention and DR-5HT\nbecause it is part of the model’s suppression. High DR-5HT strengthens the suppressive field which\nweakens attention to the cued side; Low DR-5HT weakens the suppressive field which strengthens\nattention to the cued side. This suppressive role for DR-5HT, as the reviewer acknowledges, is supported\nby prior work on 5HT receptor expression within cortical types and optogenetic studies6–15. We should\nalso note that DR neurons are tonically active and the suppression of DR neuron activity by stimuli has\nbeen reported before16.\nA final (speculative) point is that the inverse relationship between DR-5HT and attentional strength may\nmean another signal with a non-inverted relationship balances DR-5HT’s effects, creating push-pull\ndynamics that could allow rapid attentional control. Candidates for such a signal could be acetylcholine17–\n22 and/or norepinephrine23–26. Studying these neuromodulators with our task could be a way to address\nthis issue. We have integrated this speculation into our revised Manuscript Discussion.\nRev.3d. Taken together, the key finding is that DR-5HT activity is suppressed immediately before hit trials\nin a visual detection task.\nThe reviewer suggests that our key finding is the “pre-grating DR-5HT suppression on hit trials”\nWe apologize that this was not clearer, as this is not the key finding of our work. Our key finding is that\nrapid DR-5HT dynamics (high and low) causally and selectively regulated attentional strength. The pre-\ngrating suppression before hits is a subset of the data in support of this finding –the direct evidence\ncomes from studying our behavioral attention measures in response to endogenously or optogenetically-\ninduced changes in DR-5HT: attentional strength increases when DR-5HT is low before FAs and\nattentional strength decreases when DR-5HT is high.\nRev.3e. Yet, without a clear neuronal mechanism linking this activity to behavior, the broader relevance\nof the study for the neuroscience community remains limited.\nWhile we appreciate the reviewer’s enthusiasm for a 5HT mechanism in V1, we strongly disagree that it\nis required for our study to be impactful. This is because we report several novel observations that many\nneuroscience communities will find important. They are:\n1) The first report that DR and 5HT regulate visual attention. This holds immediate impact for attention\nresearch because DR-5HT dynamics could be used as a real-time readout of when mice deploy attention.\nFor cognitive neuroscientists, it motivates study into whether 5HT-modifying drugs may ameliorate\nattentional deficits that accompany disorders like ADHD and autism spectrum disorder. Thus, our findings\noffer new entry points to understand attentional circuitry and investigate therapeutic potential of 5HT\nsignaling for attentional dysfunction.\n2) Demonstration that DR-5HT specifically regulates attentional strength. This neuronal mechanism\nguides circuit dissection studies to learn how 5HT influences attentional target representations whose\ndistributed representations across frontal, parietal, and visual cortical areas match the broad DR\ninnervation of cortex. This observation also links the fields of neuromodulation, attention, and visual\nneuroscience and offers our task/behavioral methods to place other neuromodulators into a common\nframework that includes attention, internal state (arousal, motivation), and perceptually guided decisions.\nThus, our study offers a strategy to unify other candidate attention signals into a single picture and guides\nstudies into their circuit-level mechanisms.\n3) Reveals that attentional strength and focus are computationally separable. This insight would\ninterest visual and computational efforts to understand the architecture of attention computations. Our\ndata suggests that 5HT encodes suppressive field strength in the normalization model of attention, but\nour data is also consistent with 5HT encoding cue validity as captured by reinforcement learning models.\nThus, our findings suggest a way to bridge theoretical models of attention and learning.\n4) Discovery of perceptually-relevant, second-scale, DR-5HT dynamics would interest DR and 5HT\nbiologists because it challenges the view that 5HT act only on the scale of minutes to hours. Our studies\nmotivate the study of rapid 5HT dynamics in earlier paradigms to understand their role in behavioral\nresponses to punishment or patience. Thus, our study reveals avenues to understand the biological roles\nof 5HT.\nReviewer #2 (Remarks to the Author): In the revised manuscript, with all the changes and the additional\nanalysis provided, all my previous concerns now have been addressed. I have no additional concerns,\nand I support the publication of the study.\nWe thank the reviewer for their kind words and insightful suggestions. We are delighted they support\npublication of our study.\nReviewer #3 (Remarks to the Author):\nThe authors have adequately addressed all my comments.\nWe thank the reviewer for their insightful suggestions and kind words for our manuscript.\nReviewer #4 (Remarks to the Author):\nThe authors have addressed several concerns.\nWe thank the reviewer for their insightful suggestions and kind words for our manuscript.\nRev4.1. -Given that several reviewers misunderstood the FA/start lick structure it would be useful to better\ncommunicate this in the manuscript rather than just in the response to reviewers.\nWe altered the second paragraph of the results to integrate the difference between these two licks\nLicks following pure noise were false alarms (FAs). FAs occurring within the RT window after the\npresentation of zero-coherence gratings were unrewarded and used to compute psychometric measures\n(Fig. 1c). FAs during the delay period caused trials to restart until the delay period was lick-free (Fig. 1a,\nrestart). Most mice FA licked to noise and delay periods were seldom without FAs. We previously showed\nthat many FAs occur because mice react to grating-like features that emerge randomly from the noise.\nBy reverse correlating FAs to the noisy checkerboards, we saw that mice attended to the cued side for\nincreases in verticality and local contrast 36. Here, we simplified this previous model to better quantify\nvisual attention.\nWe have also clarified the label in Manuscript Fig. 2h to indicate that it is only the last 15sec of the trial.\nReferences\n1. Reynolds, J. H. & Heeger, D. J. The Normalization Model of Attention. Neuron 61, 168–185 (2009).\n2. Carandini, M. & Heeger, D. J. Normalization as a canonical neural computation. Nat Rev Neurosci\n13, 51–62 (2012).\n3. Herrmann, K., Montaser-Kouhsari, L., Carrasco, M. & Heeger, D. J. When size matters: attention\naffects performance by contrast or response gain. Nat Neurosci 13, 1554–1559 (2010).\n4. Denison, R. N., Carrasco, M. & Heeger, D. J. A dynamic normalization model of temporal attention.\nNat Hum Behav 5, 1674–1685 (2021).\n5. Li, H.-H., Rankin, J., Rinzel, J., Carrasco, M. & Heeger, D. J. Attention model of binocular rivalry.\nProc. Natl. Acad. Sci. U.S.A. 114, (2017).\n6. Michaiel, A. M., Parker, P. R. L. & Niell, C. M. A Hallucinogenic Serotonin-2A Receptor\nAgonist Reduces Visual Response Gain and Alters Temporal Dynamics in Mouse V1. Cell Rep 26, 3475-\n3483.e4 (2019).\n7. Barzan, R. et al. Gain control of sensory input across polysynaptic circuitries in mouse visual cortex\nby a single G protein-coupled receptor type (5-HT2A). Nat Commun 15, 8078 (2024).\n8\n8. Weber. Htr2a gene and 5-HT2A receptor expression in the cerebral cortex studied using genetically\nmodified mice. Front. Neurosci. https://doi.org/10.3389/fnins.2010.00036 (2010)\ndoi:10.3389/fnins.2010.00036.\n9. Puig, M. V. & Gulledge, A. T. Serotonin and Prefrontal Cortex Function: Neurons, Networks, and\nCircuits. Mol Neurobiol 44, 449–464 (2011).\n10. Eickelbeck, D. et al. CaMello-XR enables visualization and optogenetic control of Gq/11 signals and\nreceptor trafficking in GPCR-specific domains. Commun Biol 2, 60 (2019).\n11. Celada, P., Puig, M. V. & Artigas, F. Serotonin modulation of cortical neurons and networks. Front.\nIntegr. Neurosci. 7, (2013).\n12. Ju, A., Fernandez-Arroyo, B., Wu, Y., Jacky, D. & Beyeler, A. Expression of serotonin 1A and 2A\nreceptors in molecular- and projection-defined neurons of the mouse insular cortex. Mol Brain 13,\n99 (2020).\n13. Bonnin, A., Peng, W., Hewlett, W. & Levitt, P. Expression mapping of 5-HT1 serotonin receptor\nsubtypes during fetal and early postnatal mouse forebrain development. Neuroscience 141, 781–\n794 (2006).\n14. Azimi, Z. et al. Separable gain control of ongoing and evoked activity in the visual cortex by\nserotonergic input. Elife 9, e53552 (2020).\n15. Hong, S. Z. et al. Norepinephrine potentiates and serotonin depresses visual cortical responses by\ntransforming eligibility traces. Nat Commun 13, 3202 (2022).\n16. Cohen, J. Y., Amoroso, M. W. & Uchida, N. Serotonergic neurons signal reward and punishment on\nmultiple timescales. Elife 4, (2015).\n17. Chebolu, S., Dayan, P. & Lloyd, K. Vigilance, arousal, and acetylcholine: Optimal control of\nattention in a simple detection task. PLoS Comput Biol 18, e1010642 (2022).\n18. Gritton, H. J. et al. Cortical cholinergic signaling controls the detection of cues. Proc Natl Acad Sci\nU S A 113, E1089-1097 (2016).\n19. Pinto, L. et al. Fast modulation of visual perception by basal forebrain cholinergic neurons. Nat\nNeurosci 16, 1857–1863 (2013).\n9\n20. Sarter, M. & Lustig, C. Cholinergic double duty: cue detection and attentional control. Curr Opin\nPsychol 29, 102–107 (2019).\n21. Parikh, V. & Bangasser, D. A. Cholinergic Signaling Dynamics and Cognitive Control of Attention.\nCurr Top Behav Neurosci 45, 71–87 (2020).\n22. Noudoost, B. & Moore, T. The role of neuromodulators in selective attention. Trends Cogn Sci 15,\n585–591 (2011).\n23. Ghosh, S. & Maunsell, J. H. R. Locus coeruleus norepinephrine contributes to visual-spatial\nattention by selectively enhancing perceptual sensitivity. Neuron 112, 2231-2240.e5 (2024).\n24. Sara, S. J. & Bouret, S. Orienting and reorienting: the locus coeruleus mediates cognition through\narousal. Neuron 76, 130–141 (2012).\n25. Aston-Jones, G. & Cohen, J. D. An integrative theory of locus coeruleus-norepinephrine function:\nadaptive gain and optimal performance. Annu Rev Neurosci 28, 403–450 (2005).\n26. Bari, A. et al. Differential attentional control mechanisms by two distinct noradrenergic coeruleo-\nfrontal cortical pathways. Proc. Natl. Acad. Sci. U.S.A. 117, 29080–29089 (2020).\nI am very grateful for the time and energy the authors dedicated to addressing my questions.\nThe paper is much clearer in its current version regarding the underlying hypotheses, findings,\nand conclusions. I have no further questions.\nWe thank the reviewer for reading our paper and for the insightful suggestions which have\nimproved the clarity and impact of our paper.\nIn the study “Rapid dynamics of dorsal raphe serotonin neurons selectively regulate the\ngain of visual attention” by Lehnert and colleagues, the authors show that fast dynamics of\nserotonin in dorsal raphe and V1 set the attentional gain, independent of location (using\ncalcium imaging in DR, serotonin sensors in V1, and optogenetics). The paper is well written,\nthe results are clear, and the conclusions are very important and well supported by the data.\nThe discussion does a good job at linking these results with the broader literature on the\ntopic.\nI only have some minor points to raise, primarily for clarification purposes:\n P2: “Gratings switched sides after ~35 trials and had a flat probability of appearance\nin time (Fig. 1b).”\nFig 1b does not show that gratings switched nor the flat probability (I think this figure\nreference should be moved).\n What is the proportion of trials with FAs and misses?\n Is the quantification in Figure 1g in the central bin only? Or average across bins?\n(Same for Figure 3i and 5k).\n P3: “These results reveal that a second-scale elevation in DR activity precedes a miss,\nwhereas a rapid suppression precedes a hit.”\nWhy do they show that there is an elevation? With respect to hits? Or with respect\nto baseline? If the former, then this should be rephrased to reflect what the data\nshows\n P3: “These results showed that low DR activity predicts higher sensitivity to gratings\nrather than changes to impulsivity, engagement, or strategy”\nCan you clarify which results show which of these claims? Are RT and lapse rate\nassociated with engagement? Response bias to strategy, and criterion to impulsivity?\n P4: “Interestingly, sensitivity to vertical energy and local contrast remained at the\ncenter of the screen in both DR high and low (Fig. 3h). This result suggested that\nrapid DR low dynamics are correlated with increases in attentional gain rather than\nchanges in the way sensitivity was allocated across the screen.”\nThere are no stats to support this claim. Is the center-bias in DR high significant?\n(same applies to figure 4h and 4m)\n Can you add some discussion to understand diffused effects of serotonin on the\nweighted sensitivity maps of 5i-j?"}, {"round": 2, "reviewer_comments": {"reviewer_1": "I am very grateful for the time and energy the authors dedicated to addressing my questions. The paper is much clearer in its current version regarding the underlying hypotheses, findings, and conclusions. I have no further questions.", "reviewer_2": "In the revised manuscript, with all the changes and the additional analysis provided, all my previous concerns now have been addressed. I have no additional concerns, and I support the publication of the study.", "reviewer_3": "The authors have adequately addressed all my comments.", "reviewer_4": "The authors have addressed several concerns.\n-Given that several reviewers misunderstood the FA/start lick structure it would be useful to better communicate this in the manuscript rather than just in the response to reviewers."}, "author_response": ""}, {"round": 3, "reviewer_comments": {"reviewer_1": "I am very grateful for the time and energy the authors dedicated to addressing my questions. The paper is much clearer in its current version regarding the underlying hypotheses, findings, and conclusions. I have no further questions."}, "author_response": ""}]

Decision letter