One of the most overlooked populations in our society and in health care are middle-aged and older adults living with a serious mental illness (SMI) despite the growing numbers of this population. Health care communities, including both inpatient and outpatient mental health programs, have a responsibility to provide care that nurtures clients’ mental as well as physical health needs. Providing accessible and engaging physical activity programs is an excellent way to provide this type of holistic care. The purpose of this article is to describe the impact of a pilot videogame-based physical activity program on walking speed in older adults with SMI.A one-group pretest post-test pilot study was conducted with a sample of 52 older adults with SMI recruited from community-based mental health programs. Participants played an active videogame  for 50-minute group sessions 3 times a week for 10 weeks. Walking speed was assessed with the timed 3-m walk from the Short Physical Performance Battery at enrollment, 5 weeks, and 10 weeks.p ≤ .05) significant improvement in walking speed. Best estimates for clinically meaningful changes in walking speed are 0.05 m/s for a small change and 0.10 m/s for a substantial change.Participants achieved statistically significant improvement in walking speed  0.04, Upper Limit (UL) 0.15) over a 10-week period. This change represents a clinically and statistically (Results suggest that engagement in a group videogame-based physical activity program has the potential to improve walking speed in older adults with SMI. In turn, walking speed is an important indicator of premature mortality and cardiorespiratory fitness. Translational Significance: As we design interventions, we need to consider whether the intervention will be affordable, easy to acquire, and easy to implement so as not to further heighten a health inequity. Our videogame-based physical activity program is an example of an affordable intervention that can be nimbly offered in a variety of treatment settings. Our activity is an excellent intervention for inpatient and community-based mental health care environments to provide a low-cost and engaging physical activity within a supportive group setting.One of the most overlooked populations in our society and in health care are middle-aged and older adults living with a serious mental illness (SMI) despite the growing numbers of this population. Persons in this segment of the population tend to have both acute and chronic medical conditions in addition to disabilities that are attributed to medical and psychiatric origins ,2. HealtAdults living with an SMI, such as schizophrenia, experience higher rates of morbidity and mortality than the general population and die on average 8–23 years sooner than a person not living with an SMI  and theiEffective and easily accessible physical activity programs would help to reduce the disability associated with SMI and mitigate some of the other factors that contribute to poor physical health. Research has documented that middle-aged and older adults with SMI need access to engaging physical activity programs and that the lack of available physical activity opportunities contributes to poor health outcomes . Ample rIn a Cochrane review , the effVideogames with an interface that requires physical exertion to play, such as the Kinect for Xbox 360 game system , promote physical activity. The Kinect for XBox 360 system does not require the participant to use a controller. Instead, the participant uses their body to directly control the game. The Kinect sensor utilizes full-body tracking that recognizes the participant’s body and mirrors movements in the game. In 2011, the American Heart Association convened a scientific panel that examined whether videogame-based physical activity improved health-related skills and health behaviors . The panIn this article, we present the results of a 10-week videogame-based physical activity program designed for middle-aged and older adults with SMI. Our program was designed and led by a PhD-prepared advanced practice nurse. In our program, 3 times a week for 10 weeks, participants attended 50-minute active group video game sessions using the Kinect for Xbox 360 game system. One of the aims of the program was to determine preliminary efficacy on walking speed. Walking speed is an important indicator of premature mortality and cardiorespiratory fitness . ImproveThe 10-week study was designed as a pretest post-test with no control group. This pilot work is intended to inform a larger RCT. Institutional review board approval was obtained from the sponsoring university’s human subjects committee. Anonymity and confidentiality were maintained according to the guidelines set forth by the sponsoring university’s human subjects committee.Study participants were recruited from the community, direct contact with potential subjects (who previously gave consent to be contacted for future research), and from an outpatient mental health clinic and a transitional residential facility. All potential participants who self-referred or were referred to or recruited by the Principal Investigator (PI) or research assistant (RA) were assessed for the eligibility criteria that consisted of speaking English, 45 years old or older, have a diagnosis of SMI , or major depression), or have symptoms that indicate risk for psychosis . All participants passed a capacity to consent test based on comprehension of the consent form and gave informed consent. The capacity to consent assessment included asking a series of questions which included: What is the purpose of this study? Do you have to take part in the study or is it alright to say no? Participants were excluded if they had known medical conditions or other physical problems that required special attention to engage in an exercise program .For 10 weeks, participants played an active video game using the Kinect for Xbox 360 game system  in 50-minute group sessions, 3 times a week. Participants used their body to directly control the game via full-body tracking sensors that mirror movements rather than using a remote control. About 6 feet of free space between the participant and the XBox Kinect sensor was required. The group sessions took place in a multipurpose room at a transitional residential facility or an outpatient clinic. The use of easily available, off-the-shelf video games made it possible for older adults with SMI to participate in physical activities in a group setting on-site at a mental health facility.Bowling from the Kinect Sports game DVD. Participants were given the option to choose from 10 games at the following sessions, with all participants eventually playing the same 10 games. These game options included Kinect Sports , Kinect Dance Central 2, Kinect Adventures , and Kinect Your Shape Fitness Evolved (Tai-Chi and Body Focus Workouts). One session a week was dedicated to games that promoted coordination and balance, a second session on games that promoted aerobic activity, and the third session on games that promoted strength and flexibility. Participants began the 10-week sessions with single-player turns, so they could learn how to play the games. Some multiplayer games, such as skiing, allowed for groups of up to 6 players to play at the same time. Single-player sessions were still conducted in a group format, such as with bowling. One player bowls at a time, yet the game can be competed as a group. As the participants became more adept at the games, multiplayer games were encouraged, so participants could play for longer periods of time. Group size was capped at 6 players to allow for each player to have an adequate amount of time per turn. Participants were remunerated with 5 dollars at the end of each session for the time needed to complete the sessions and assessments. Upon completion of each session, players rated their perceived exertion with the Borg perceived exertion (BPE) scale  and were instructed to stop exercising if these warning signs occurred. The PI and/or staff were present during all sessions to assist with game set up and to monitor participants. Each 10-week session started with E) scale . The BPEAssessments were completed prior to starting the videogame-based physical activity program and after 5 and 10 weeks of the program. Participants completed questionnaires and participated in assessments about clinical and sociodemographic information. Study staff measured the outcomes and were not blinded. This represents a study limitation yet was necessary as part of a small pilot feasibility study.Participants’ overall mobility was assessed primarily with the Short Physical Performance Battery (SPPB) . The SPPIn addition, the 6-minute walk test (6MW)  was perfMultilevel linear regression models were used to test change across time for walking speed ,25. A beThis method provides unbiased parameter estimates if the missingness is ignorable. For example, if participants only provide data at the initial assessment, their data contribute to the estimation of the intercept  and intercept variance. Participants’ information will contribute to the analysis as many times as they provide data. Any missing data that did occur are assumed to be missing at random (one type of “ignorable”) ,28. The z-test using bootstrapped standard errors assuming normality, percentile-based confidence intervals, or nonparametric bias-corrected confidence intervals (BC CI). Our analyses presented here employed the nonparametric bootstrap in order to obtain nonparametric bias-corrected bootstrapped confidence intervals using 5 000 repetitions. In using bootstrapped confidence intervals to draw conclusions about statistical significance for our quantitative outcome, a regression coefficient or other effect was determined to be significant if zero was not in the interval. Our analyses were carried out with Stata/SE Release , and so the general linear model assumption of normal errors was not justified ,25. Ther Release , 29, usin = 27) or past smokers (n = 16). Twenty-nine percent of participants completed at least 3/4 of the sessions and 62% of participants completed at least 1/2 of the 30 sessions. Three participants had perfect attendance.Fifty-two participants were included in the analyses. As Results of the multilevel linear regression are given in The predicted mean score for 6MW from the linear analysis for the sample at enrollment was 388 m. For each additional 5 weeks of engaging in the physical activity program, meters walked in the 6MW test increased on average 1.40 m  over the entire 10-week study period as illustrated in Our most important finding was that walking speed on a 3-m test changed over a 10-week physical activity program. Furthermore, our program was engaging, accessible, and introduced participants to activities they can continue beyond the intervention. We adapted the environment where people received care to create more opportunities for physical activity. Creating these types of environmental supports and changes is a necessary step in providing holistic care for people living with an SMI.n = 10) was trained with 2 soccer training sessions per week. One of the outcome measures was a 30-m sprint test and slalom test running with a ball. The TG improved significantly on a 30-m sprint test and slalom test running with a ball from baseline to post-test when compared with CG. Battaglia et al. also found the social aspect to be an important feature of their soccer program, attributing some of the success to team activities such as the opportunity for social interaction, sharing leisure activities, competition, and encouraging teammates. We also showed that the social nature of the groups was one of the reasons people were looking forward to returning to each game session  or playing computer games (n = 7). Participants engaged in the programs 3 days per week for 8 weeks. HIT included 4 × 4 minute intervals with 3-minute break periods. Mechanical efficiency of walking (defined as the percentage of work input that is converted into work output) improved by 12% in the HIT group and the improvement was significantly more than the CG group (p = .031). Like our study, a change in walking outcomes was found over a relatively short period of time.Heggulund et al.  conducteSD = 11.7). The investigators found a large effect size (ES) increase in cardiorespiratory fitness as measured by the 6MW test from baseline to midpoint (ES = 1.17) and baseline to post-test (ES = 1.22). Out of the 13 participants with complete data, 8 at midpoint and 10 at post-test achieved the prespecified threshold for achieving clinically significant improvement (>50-m increase). Similar to our study, the program was group-based and it was offered multiple times per week. Our results stand out from these studies because we include a population that is often excluded from research in schizophrenia .Browne et al.  conducteWe are limited by our small sample size and the lack of statistical or clinical significance on walking speed over a longer distance and SPPB total score. In addition, the lack of a control group limits inferences about our findings. However, we expect with a larger sample to find a more reliable estimate of walking speeds. In addition, the effect we found at the current sample size reduces concerns about sampling error.The group-based nature of our videogame-based program addresses one fundamental cause of socioeconomic inequalities in health and mortality, namely lack of social support. As Link and Phelan  discussePhelan et al.  also sta


Neocortex is classically divided into distinct areas, each specializing in different function, but all could benefit from reinforcement feedback to inform and update local processing. Yet it remains elusive how global signals like reward and punishment are represented in local cortical computations. Previously, we identified a cortical neuron type, vasoactive intestinal polypeptide (VIP)-expressing interneurons, in auditory cortex that is recruited by behavioral reinforcers and mediates disinhibitory control by inhibiting other inhibitory neurons. As the same disinhibitory cortical circuit is present virtually throughout cortex, we wondered whether VIP neurons are likewise recruited by reinforcers throughout cortex. We monitored VIP neural activity in dozens of cortical regions using three-dimensional random access two-photon microscopy and fiber photometry while mice learned an auditory discrimination task. We found that reward and punishment during initial learning produce rapid, cortex-wide activation of most VIP interneurons. This global recruitment mode showed variations in temporal dynamics in individual neurons and across areas. Neither the weak sensory tuning of VIP interneurons in visual cortex nor their arousal state modulation was fully predictive of reinforcer responses. We suggest that the global response mode of cortical VIP interneurons supports a cell-type-specific circuit mechanism by which organism-level information about reinforcers regulates local circuit processing and plasticity. Neocortex contains a number of functionally distinct areas such as the visual, frontal, and motor (Mtr) cortical regions, each specializing in different roles . ClassicVasoactive intestinal polypeptide (VIP) expression demarcates a small interneuron subpopulation (15–20%) located mostly in the upper layers of the cortex . PreviouVIP interneurons have been shown to have a multiplicity of roles in sensory processing, arousal modulation, learning, and plasticity. First, studies in the primary sensory regions – barrel cortex, ACx, and visual cortex – have demonstrated that tactile, auditory, and visual stimuli drive VIP neuron activity in diverse ways . HoweverWe sought to investigate common rules that recruit VIP interneurons. Our starting point was the observation that auditory cortical VIP neurons respond not only to auditory stimuli but also to reward and punishment . VIP celTo probe the behavioral function of VIP interneurons, we trained head-fixed mice (n=22) on a simple auditory discrimination task . Each trTo study the reinforcer-mediated dynamics of VIP interneurons across the cortex, we sought to simultaneously record a large number of VIP cells, across a large cortical volume. Because of their sparse cortical distribution, electrophysiological methods combined with optogenetics-assisted identification are less suitable for cortex-wide recordings of VIP interneurons. To overcome this challenge, we used random access, 3D AO two-photon microscopy . This meWe first focused on measuring the calcium-related activity of VIP interneurons in the medial parietal association area (MPta). We then extended recordings of VIP interneurons to most of dorsal cortex including visual, SS, Mtr, and parietal areas . We founTo probe additional deep lying cortical structures, we took advantage of the coherent recruitment of VIP interneurons by reinforcers and used fiber photometry . This apPta-V1=3.22, Mann-Whitney test, p=3.77 10–9), while the opposite was observed for the projection associated with the ‘slow’ group . However, we did not observe any differences in the distribution of the different clusters across depth .The difference in dynamics at the population level across different brain areas might be supported by heterogeneity in the individual response profiles of VIP interneurons. Thus, we sought to characterize the dynamics of VIP interneurons at a single-cell resolution and across dorsal cortex using chessboard 3D AO recordings. We first focused on VIP interneurons activated upon reward delivery during the sensory discrimination task (n=606 cells). We applied principal component analysis (PCA) to the average reward responses of individual neurons to reward. We then clustered these responses using k-means clustering. This approach did not readily separate neurons according to the recording sessions . Rather,2=97.7%, F=206.4, p<0.001, n=8 mice n=439 cells, The groups delineated by the PCA-based cluster analysis possibly represent distinct subtypes with variable genetic and connectivity patterns. We addressed subtype diversity by measuring the diameter  of the somata and found bimodal distributions both at the level of individual mice (7/8 mice) and also the entire population examined . The third latent temporal component showed a slower time course, with some reward specificity and was overrepresented in neurons from the visual cortex . TCA allowed us to quantify on the one hand, the brain-wide recruitment of VIP interneurons by reward- and punishment-associated responses and, on the other hand, brain region-dependent heterogeneity in dynamics  in an unbiased manner.The differences in average response dynamics from individual neurons could also arise from differential variability across trials. To evaluate this potential heterogeneity in the single-trial dynamics of VIP interneuron activity, we used tensor component analysis TCA, . TCA allBeyond neuron-to-neuron and inter-trial variability, inter-individual  variability can also contribute to the heterogeneity experienced in the responses. Therefore, we calculated the coefficient of variation (CV = SD/mean) of the peak of responses among the neurons of individual recordings (cell-to-cell) and compared the result to the CV calculated using the average peak amplitudes of the sessions . We conducted this analysis in parietal cortical measurements  separately in the four trial types . The celR=0.62, 2=39.0%; p=0.006). For comparison, the cue response was not influenced by the location of the imaging site  from where the activity of the VIP interneurons was recorded .Differences in individual animal performance of the discrimination task could also contribute to the heterogeneity in the activity of VIP interneurons. Hence, we tested whether differences in hit rate influenced the response of VIP interneurons during various epochs. We observed a positive correlation between the hit rate and the magnitude of the cue response of VIP interneurons during Hit trials R=0.62, . Using aReward and punishment can induce changes in the arousal states of the animals, and the activity of VIP interneurons is known to be modulated by the arousal states . TherefoHit and FA trials were first split into two groups using the mean reinforcer-mediated pupil dilation for threshold .Additionally, our behavioral paradigm did not encourage mice to run, hence their small movements produced only weak modulation in VIP activity , measured over a longer temporal interval. Nevertheless, modulation was similar when restricted to the reinforcement time epoch 0–2 s, . Taken tThe circuit basis of the global signal is not yet known, although neuromodulatory systems are prime candidates, in particular, the forebrain cholinergic system. Indeed, central cholinergic neurons convey rapid reinforcement responses to cortex , and a tVIP interneurons have been found to target excitatory neurons as well , and theIn summary, we identified a cortex-wide or global activation mode for VIP interneurons, separate from their local circuit function specialized for each cortical region. By signaling reinforcement events, the global activation of VIP interneurons early in learning, may form a new information channel for cortical learning and explain how organism-level information about reinforcers regulates local circuit processing and plasticity. VIP interneurons specialize in disinhibition control over principal cells, producing selective amplification of response and gating of dendritic plasticity. This process could enable associating distant, co-active cortical ensembles predicting significant outcomes, whether reward or punishment. In other words, the VIP-mediated feedback signaling could provide the required global learning signal to strengthen functional connectivity across cortical representations. In this way, VIP neurons may transiently dial up the gain on learning, similar to how different learning phases are controlled in deep neural networks in artificial intelligence .tm.1(cre)ZjhVip/J, B6.129P2-tm1(cre)ArbrPvalb/J, FVB/N-Tg(Thy1-cre)1Vln/J, The Jackson Laboratory) housed in small groups of 2–4 under controlled temperature and humidity conditions. They were kept on a reverse light cycle, and during the training and the experimental period the water consumption of the VIP-Cre and PV-Cre mice was restricted to 1 ml/day after recovering from surgery. The mice had ad libitum access to food.All experimental procedures were carried out following the guidelines of the Animal Care and Experimentation Committee of the Institute of Experimental Medicine of the Hungarian Academy of Sciences, and the Cold Spring Harbor Laboratory Institutional Animal Care and Use Committee, in accordance with the Hungarian, EU, and National Institutes of Health regulations (reference number: PEI/001/194-4/2014). We used male and female adult (6–24 week-old) VIP-Cre, PV-Cre, and Thy-1-Cre mice . Ropivacaine 0.2% was administered subcutaneously over the skull prior to the incision. After removing the skin over the top of the skull, which was then thoroughly cleaned and dried, a round craniotomy was performed using a 3-mm diameter biopsy punch and a dental drill. After the bleeding had been stopped, a double coverslip was implanted over the cranial window and fixed using a mixture of cyanoacrilate glue (Loctite Superbond) and luting cement (3 M ESPE RelyX). Finally, a metal headbar was cemented to the skull using dental cement (C&B Superbond). The 3-mm diameter cranial window was positioned according to two main aspects. We centered the craniotomy on the injection site, except in Mtr and visual areas, where this would have resulted in transecting the sutures, which would have caused larger motion artifacts and severe bleeding from venous sinuses. During the procedure, the mice were head-fixed and laid on a heating pad to maintain stable body temperature. After the operation, the mice were woken up using a mixture containing nexodal, revertor, and flumazenil , and put on another heating pad where they stayed until recovered enough to be finally put back in their home cages. Post-operative care consisted of a daily intraperitoneal carprofen injection  for up to 5 days, and subcutaneous Ringer lactate injection (0.1–0.15 ml) to prevent dehydration. The cranial window implantation was usually performed 2 weeks after the virus injection. Injection sites of the 18 dorsal cortical regions from 16 mice were defined on the basis of coordinates from the Allen and Paxinos brain atlases . In the Anesthesia and post-operative care were executed as above. A small, approximately 0.5-mm diameter craniotomy was performed with a dental drill. 200–300 nl AAV9.Syn.Flex.GCaMP6f.WPRE.SV40 (Penn Vector Core) was injected using a borosilicate pipette at 350 µm depth into different cortical areas for two-photon imaging. The speed of the injection was 15–20 nl/s, and there was a 10-min period between the end of the injection and the removal of the pipette to prevent leakage. We injected one to two areas per animal.2 – 0.8% isoflurane) and placed in a stereotaxic apparatus. A small craniotomy was performed using a dental drill above the left ACx  and the right mPFC . 200 nl of AAV9.Syn.Flex.GCaMP6f.WPRE.SV40 (Penn Vector Core) was then injected at a rate of 50 nl/min into the ACx (1.2 mm deep) and in the mPFC (1.5 mm deep). The fiber optic cannulas  were inserted 0.4 mm above the injection sites for both locations and sealed in place using Metabond, Vitrebond, and dental acrylic. Behavioral training and physiological recordings were started at least 2 weeks after surgery to allow mice to recover and the fiber to clear.Animals were anesthetized using isofluorane  coupled to an optic fiber (M75L01) and collimation lens (F240FC-A) for GCaMP6f excitation. The 470-nm excitation light was delivered to the cannula implanted on the head of the animal using a second collimation lens (F240FC-A) coupled to a 400 µm, high NA, low autofluorescence optic fiber . The emission light was collected using the same optic fiber and directed to a Newport 2151 photoreceiver using a focusing lens . Excitation (ET470/24 M) and emission (ET525/50) filters, and a dichroic mirror (T495LPXR) were purchased from Chroma Technology. The 470-nm excitation light was amplitude-modulated at a frequency of 211 Hz, with a max power of 40 µW, using an LED driver (LEDD1B) controlled through a National Instrument DAQ (NI USB-6341). The modulated data acquired from the photoreceiver were decoded as in Mice were kept on a limited access water schedule for behavioral experiments. They had to lick when they heard a go tone  to get small water droplets (5 µl) as a reward, and avoid licking after hearing a no-go tone  which was associated with a 100-ms long mild air puff aimed into the eye. The absence of licking in go trials was not rewarded . If the animal correctly withheld licking to no-go tones (CR), the air puff was omitted. The time interval between tone and reinforcement onset was a function of the reaction time of the animal. In the figures, responses were aligned to reinforcement onset before averaging in FA and Hit, which resulted in a jitter in cue onset time. Reinforcement came 0.5 s after it was triggered, except for during the fiber photometry recordings where reinforcers were delivered upon licking (see below). The intensity of the air puff was set to yield a blink response. In some experiments, we introduced two additional stimuli that were less easy to discriminate (8 kHz for go and 10 kHz for no-go tones). The addition of these cues did not reveal any significant differences in GCaMP6f signals in VIP neurons, therefore these trial types were combined as go and no-go stimuli for further analysis. Licking was detected using a custom-made infrared sensor. Behavioral data were acquired using a Bpod device , and the tones were generated using a PulsePal device  and Logi2) and third-order  dispersion compensation . The 4DBCU unit was fine-tuned to provide the best image contrast and SNR at each wavelength in the depth. The first two water-cooled AO deflectors were filled with chirped acoustic waves whose frequencies form two orthogonal electric cylinder lenses (AO z-focusing unit). The second group of AO deflectors, with 15-mm clear optical aperture (Gooch and Housego), did the majority of lateral scanning and also compensated for the longitudinal and lateral drift of the focal spot in cooperation with the first two deflectors according to The improved microscope is designed and constructed based on the previous system reported earlier  when AO-based 3D ROI scanning is used instead of point-by-point volume scanning.where 2+ responses. Moreover, as the pixel dwell time is 243-fold lower we would collect less signal from one pixel, resulting in a 41,506-fold decrease in the product of SNR2 and measurement speed (https://www.physikinstrumente.com). This resulted in a measurement speed of ~1 Hz and in 6654-fold decrease in the product of SNR2 and measurement speed when compared to 3D chessboard scanning . However, both 3D chessboard scanning and volume scanning capture multiple voxels from a single VIP neuron (in our measurements for chessboard scanning: 105.2 ± 0.4 voxels/neuron and for volume scanning: 338.5 ± 0.1 voxels/neuron). Therefore, in a more precise calculation we need to divide the improvement shown in Table S1 for chessboard scanning with the ratio of 338/105.Second: piezo actuators and resonant scanners are mechanically never perfectly balanced and are also sensitive to local mechanical vibrations and thermal turbulences which result in tumbling, wobbling, and jitter in the laser scans. These mechanical effects are difficult to precisely quantify into fluorescence changes although they would compensate the first factor. Therefore, for simplicity of calculation, both factors were ignored in our calculations.Random-access targeting of ROIs by AO scanning is useful not only in 3D but also in two-dimension (2D). Because the ratio of the VIP cells in the cortex is about <1% we can estimate the increase in measurement speed and SNR in 2D as follows:A liquid-crystal display (LCD) monitor was placed at a distance of 20 cm from the contralateral eye of the mouse, spanning 100° × 70° of the visual field. The objective was covered with a black rubber shield to prevent stray light entering through the gap between the animal’s head and the objective. A visual stimulation protocol was written in Matlab using the ‘Psychtoolbox’ package. The protocol consisted of eight differentially directed gratings with an angular interval of 45°. At the beginning of each trial, a gray screen was presented for 20 s. After that, a grating appeared and remained still for 1 s, and then moved orthogonally to its orientation for 6 s at 1 cycle/s speed, then it stayed still for 1 s, and finally the gray screen reappeared again. Gratings were repeated 10–20 times per direction in pseudorandom order. Pyramidal cell data were obtained from Thy-1-Cre mice.Motion correction, selection of ROIs corresponding to VIP cells on the frames of 3D chessboard scanning, background calculation, ΔF/F calculation, filtering, and data visualization were performed using the MES data acquisition software written in Matlab and C++ (Femtonics). Motion correction, if necessary, was conducted with a custom-written offline motion correction algorithm (see Off-line motion corrections section), and remaining artifacts were interpolated or smoothed with partial Gauss filtering under visual control.For the trial-to-trial analysis, we considered a neuron in a given trial as active if the difference between the peak ΔF/F value of reinforcer delivery epoch  and mean ΔF/F value of baseline epoch  was higher than two SD. Peak ΔF/F value was defined here as the average ΔF/F value of the datapoints around the peak in the range of 250 ms. The results presented in We used linear regression models to address heterogeneity of the cue responses . The exp2+ responses recorded during Hit trials were extracted. 16 cells were excluded from this analysis as they disproportionately increased the reconstruction error. Data were z-scored using the mean and variance of fluorescence during the first second of recording. We further normalized using the maximal amplitude of the response calculated during the period from 0 to 4 s after reward delivery. We applied a dimensionality reduction along the time axis using a PCA on the period from 0 to 4 s after reward delivery. We then considered only the first four principal components (PCs) explaining 90% of the data for clustering purposes. K-means clustering with five replicates was used to cluster into five types the PCs of the responses of VIP interneurons.The clustering analysis  was doneWe estimated cell diameters by recording the fluorescence profile of the somata placing two lines along the longest and the shortest diameters . Then th2+ responses recorded during the discrimination task. After smoothing, single-trial neural activities corresponding to the reaction time periods for Hit and FA trials were time-warped to a fixed 1.5 s/30 data points in length. This step was necessary because (1) the reaction time varied from trial to trial; and (2) the variable number of neurons recorded in the different session led to a variable sampling rate for imaging. These factors led to a trial- and session-dependent number of images between the cue and the reward delivery. All recordings were rendered non-negative by subtracting the minimal fluorescence ΔF/F value for each cell. Data were finally normalized by dividing by the average maximum fluorescence ΔF/F value of Hit-only trials. Only the first 10 trials of each types were then selected for each cell and each session and assembled in a N×T×K matrix where N=771 neurons, T: time (s), K=40 trials. TCA reconstruction error was computed with different latent numbers  with 10 different initial conditions for each latent number. Using three latents led to a reconstruction error of 22%.We applied TCA  on somatA generalized linear model was built to determine the contributions of the behavioral events to the VIP interneuronal activity . This mo∆P/P = (P(t)−P0)/P0 using a 2-s period before tone onset as baseline (P0). Trials of each outcome were separated to high- and low-arousal groups on the basis of the change in the pupil diameter. The area under the pupil diameter curve was calculated in the 0–3 s interval after reinforcement onset, and the median value was selected. If the area under the curve value of a given trial was higher or lower than the median value, it was considered to be high- or low-arousal trial, respectively. For baseline pupil analysis, when we compared the VIP cell activity after reinforcement associated with low and high baseline arousal levels, the trials were again separated into low- and high-arousal trials, but here, the basis of the separation was the area under the pupil diameter curves in the  s interval before the tone onset. In The video recorded with the camera was first thresholded to isolate the pupil on the image. The pupil area was fitted to an ellipse, and the main diagonal was extracted. Missing frames caused by spontaneous or air puff-triggered blinking were interpolated manually. A Gaussian filter was applied to smooth eye movement-related artifacts. In analyzing the change of pupil diameter, the traces were normalized to Velocity traces were first Gauss filtered. They show the absolute speed of the movement, regardless of the direction. In Hit and FA trials, we defined the change in the running speed as the speed difference between the reinforcer delivery time period  and baseline time period . In Miss and CR trials, the speed difference was calculated between the tone delivery time period  and baseline time period . Trials were separated into low and high speed change groups according to the median speed change value.pref − Rortho)/(Rpref + Rortho), and DSI = (Rpref − Ropp)/(Rpref + Ropp) (pref denotes the amplitude of the response to the preferred orientation (OSI) or direction (DSI), Rortho denotes the response to the orthogonal orientation in the OSI formula, and Ropp refers to the response to the direction that is opposite to the preferred one.OSI and DSI were calculated as OSI = (R + Ropp) , where R2+ responses was then evaluated and compared to a stimulus-free baseline (−2–0 s before stimulus onset). The statistical significance of the activation and suppression was determined by a p-value cutoff of 0.05. First, the mean baseline values of each trial were subtracted from each Ca2+ trace in order to directly compare the effect of stimuli on Ca2+ responses and minimize the effect of unknown sources of noise. Lilliefors normality test was used to evaluate whether the Ca2+ signals of individual VIP neurons followed a normal distribution. The Lilliefors test showed that 76% of VIP neurons  followed a normal distribution. The fraction of the neurons activated by reinforcers (see below) with normal distribution (91%) was similar to that of activated neurons with non-normal distribution. Therefore, we used a one-tailed one sample t-test to classify the activation and suppression . Neurons were classified as responsive when either activation or suppression was statistically significant. Student’s t-test  was also used to compare calcium responses associated with low and high arousal, or low and high running speed. PCA loadings of different areas and TCA trial factors of trial types with and without reinforcement were compared with Mann-Whitney test. If not otherwise indicated, data are presented as mean ± SEM.For all analyses, the activation/suppression period was set to be 0–2 s after stimulus onset: the statistical significance of the change of CaIn the case of chessboard scanning, neuronal somata were selected from a z-stack, then the selected square ROIs were arranged as a 2D chessboard. Since the motion of a single frame during the scanning period as well as the relative rotation between subsequent frames, were not relevant, the transformation to be corrected could be approximated by a simple translational transformation between the scanning periods of the different frames. For efficiency, an algorithm based on fast Fourier transformation was used . The temIn some cases, images were also preprocessed, by either adaptive histogram equalization or simple median filtering. The image registration algorithm also provided the error of matching the moving images to the template images. As the drifting and scanning parameters were identical for each scanned ROI, we calculated the final displacement vector as the median of a fixed percentage of all ROIs with the smallest matching errors.https://github.com/QuentinNeuro, -expressing interneurons from several cortical regions with high temporal resolution. The landmark message conveyed by this manuscript is that many VIP-expressing interneurons respond to reward and punishment but also show regional differences. The conclusions drawn are generally supported by the data. This paper is of potential interest to neuroscientists expert in cortical circuitry and behavioral role of neuron types. public reviews designed to be posted alongside the preprint for the benefit of readers; ii) feedback on the manuscript for the authors, including requests for revisions, shown below. We also include an acceptance summary that explains what the editors found interesting or important about the work.Our editorial process produces two outputs: i) Decision letter after peer review:eLife. Your article has been reviewed by 3 peer reviewers, and the evaluation has been overseen by a Reviewing Editor and Tirin Moore as the Senior Editor. The following individual involved in review of your submission has agreed to reveal their identity: Francesco Ferraguti (Reviewer #2).Thank you for submitting your article "Cortex-wide fast activation of VIP-expressing inhibitory neurons by reward and punishment" for consideration by The reviewers have discussed their reviews with one another, and the Reviewing Editor has drafted this to help you prepare a revised submission.Essential revisions:1) The submitted data are potentially interesting but preliminary as the activity of VIP+ interneurons is analyzed only in one animal. In order to make the data less anecdotical, data from more animals need to be shown.2) A revised version of the manuscript should be able to distinguish between reinforcement and arousal, since the reinforcements used in this study may cause arousal3) The manuscript should assess whether well-trained animals display large responsiveness of VIP neurons to reward or reduced responses.4) The authors should revise their interpretation of the data based on the current view of cortical VIP+ cells as a diverse population of neurons that mediate both inhibition and dis-inhibition.5) Data analysis and representation should improve according to reviewers comments – see for example comments of reviewer 3 to submitted figures.Reviewer #1 (Recommendations for the authors):1. The authors need to consider the current view of cortical VIP+ cells as of providers of both inhibition AND disinhibition . In fact, VIP+ cells are highly heterogeneous, and their diversity and functional role across different cortical areas have been highlighted . This is especially important given functional heterogeneity in the response profiles of VIP+ cells observed in the current study across cortical regions. With the help of PCA and k-means clustering method, recorded VIP interneurons were categorized into 5 groups based on their temporal dynamics. These cells possibly belong to different subtypes of VIP interneurons. Post-hoc identification of these neurons using IHC would possibly provide more information on whether the 5 groups of neurons belong to distinct subtypes of VIP cells and ultimately if there are any differences in the distribution of VIP subtypes across depth.2. Was there a small fraction of VIP+ cells activated by all tasks ? Not sure, why the AVG response/area is shown. As different cell types were sampled, the best would be to arrange them by depth/layer using the full advantage of the fast volumetric imaging . Also, cells could be arranged by their soma size given that CR+/VIP+ have a smaller soma when compared to VIP basket cells .3. TCA allowed authors to understand the variability in the activity dynamics due to inter-trial variability. The interpretation of the obtained results should be added to the main text  for better understanding.4. VIP interneurons from all regions under investigation but the auditory cortex showed arousal and locomotion modulation of reinforcement-mediated recruitment of VIP interneurons. The possible reasons need to be discussed in the main text. Similarly, the absence of arousal (pupil diameter change based) modulation of reinforcement response for Hit & FA trials  requires explanation.5. Fiber photometry recordings from the auditory cortex  reveal higher calcium transients for Hit and FA compared to CR and Miss trials, however, the data from Figure S4A shows higher ΔF/F (%) for Miss & CR compared to Hit & FA. Whether the results in Figure 2A and Figure S4A were obtained from different sets of mice should be mentioned.6. Authors conclude in the Discussion section that – only the late response phase showed a correlation with the pupil size, whereas the initial, transient phase followed more closely the reinforcer delivery, however in the Figure 3 D-E, ΔF/F (%) for Hit & FA from high vs low pupil size change trials in the late response phase (~2-3 sec from the outcome) show roughly similar responses . The clarification of how the initial and late response phase was defined is necessary for better understanding. A graph comparing the responses (ΔF/F) of VIP interneurons for high vs low pupil size change trials in initial vs late phase would clarify the extent of the influence of arousal on reinforcement-mediated recruitment of VIP interneurons. Moreover, comparison of ΔP/P in Figure 3C and Figure S4A reveals that reinforcement stimuli trials (FA & Hit) lead to higher pupil size change compared to Miss & CR trials. Considering the modulation of VIP recruitment due to arousal (pupil size change), the reinforcement-mediated recruitment of VIP interneurons might be strongly influenced by arousal. More clarification is needed to conclude about reinforcement-mediated VIP recruitment.7. In the Discussion section (line 440-441), the authors claim that the VIP activity modulation due to locomotion is weak, but the comparison of activity change due to arousal  and locomotion  shows that the ΔF/F (%) values in all brain regions for arousal and locomotion modulation are comparable. The claim of weak locomotion modulation of VIP activity is not supported by the results. Also, speed modulation was assessed at a very low animal speed . Have authors tried to look at higher speed locomotion, which has been used in the previous studies?8. The authors need to discuss the potential mechanisms of co-activation of VIP+ cells (lines 169-173).Reviewer #2 (Recommendations for the authors):– At least for one area (e.g. MPta) the interindividual variance of VIP+ interneuron activity to the different trials and responses has to be provided. This would importantly add to the understanding of the sources of heterogeneity in the recruitment of VIP+ interneurons.– The stability of the VIP+ interneuron response to Hit and FA across trials should be shown.Reviewer #3 (Recommendations for the authors):A discrepancy exists with published data that do not show large responsiveness of VIP neurons to reward in visual and barrel cortices. The authors hypothesize that VIP neurons might only respond to reward and punishment during learning. This is the reason they use mice in the phase of task acquisition, that still have "enough" FA trials, while the published studies measure VIP activity in overtrained mice. To solidify this suspicion, the authors could show that, once their mice have fully mastered the task, the reinforcer responses in VIP cells are in fact reduced or absent. This could harmonize their results with these published studies and significantly strengthen the paper.Figure 1: Why are the averages in Figure 1 C and D not corresponding? From the legend it appears all curves should be the averages of the above shown 120 VIP cells. Yet their relative amplitude, as well as time course is definitely different. The hit-curve in 1C seems to start to rise before reward delivery, yet this does not seem to be the case in 1D. In 1D it is unclear what the CR and miss trials are aligned to. Is there really error bars on the bottom plots in 1C as mentioned in the legend?Was MPta chosen for this figure simply because it yielded the biggest n? In Figure 2 it appears to almost be an outlier of all the imaged areas, as it is one of very few areas where VIP neurons appear to entirely lack sensory responses.Figure 2: In panel A it is exceedingly hard to distinguish the thin lines from the same color thick lines and thus see potential sensory responses in any of the areas, perhaps one could change the hue slightly or use the different shade of blue as in panel H? In general, a different color scheme could be considered for accessibility reasons. And a legend should be added.For the speaker sign at the bottom of B it is unclear where the start of the auditory cue is, and if it is the same for all the lines of a plot.The time courses here, as well as the ones in F make it seem as though in this data set there was a fixed interval between cue presentation and reinforcer delivery. Or is it similar to π et al., where the animals triggered the reinforcer by licking. This is not mentioned anywhere.One session in M1 (hits), as well as one session in M2 (FAs), VIP neurons seem to respond before the onset of the auditory stimulus (judging from the other areas time courses), why is this?All abbreviations should be explained in the legend Figure 3 C,D,E: why were these areas chosen, or which areas are included in each of these plots? Do differences between hits and FAs exist? There appear to be potentially interesting areal differences.In the figures of the time courses of the ACx repsonses (2A and S1D) it seems like VIP cells only have sensory responses in miss and CR trials. Why is this bump absent from hit and FA trials?line 227: How are these 606 reward delivery sensitive cell related to the 622 in Figure 2?For the arousal and running modulation: why is the data split here and no correlation calculated as for the relationships to behavioral performance and sensory tuning?The running paragraph: it needs to be clarified whether they look at running speed per se, as much of the paragraph is written (fast and slow running conditions), or at speed changes, as the beginning of the paragraph states and the values (partly negative) would make it seem. If it is indeed speed change, they should rename the conditions.Figure S2F seems very important. Was also uncued negative reinforcement presented? How does the uncued data compare to the cued?Is S5A the exact same data as in 4D, just plotted in a different way? A statistical test should be performed to judge the significance of this correlation.The methods need to be improved. As is there is not enough detail and analyses cannot be understood properly. For example: What does smoothing according to the reaction time periods mean? Reaction time periods are not even mentioned in the description of the task. Please explain time-wrapping. Why is 1.5s 30 data points when the sampling rate is 27.7 Hz and not 20Hz? Why are only 10 trials selected? Why the first? Why is K then 40?Supplementary figure S3C is never mentioned. Essential revisions:1) The submitted data are potentially interesting but preliminary as the activity of VIP+ interneurons is analyzed only in one animal. In order to make the data less anecdotical, data from more animals need to be shown.We thank the reviewer and the editor for pointing out this potential misunderstanding in the method description. The AOD data presented in this manuscript are actually from 16 animals. Each animal has been recorded in one (14 mice) or two areas (2 mice). We have 4 recordings from the primary somatosensory cortex, 6 recordings from the primary and secondary motor cortices, 4 from the lateral and medial parietal cortices, and 3 from the primary visual cortex. Fiber photometry has been performed in a different 6 animals hence totaling 22 mice. In addition, our measurements were robust at the level of individual sessions: fluorescence signals were recorded in a high number of trials from multiple neurons. More specifically, Figure 1 shows recording of 128 trials from 120 neurons resulting in a total of n = 120 × 128 = 15,360 transients. Our main data set  is based on 811 cells × 95-195 trials ≈121,650 transients. Across all these animals and brain areas, we observed a consistent recruitment of VIP interneurons by reward and punishment. Although we reported the number of mice and sessions the recordings were obtained from, to clarify this misunderstanding we have now repeated this several times at the beginning of the Results section and in the figure legends to highlight the robustness of our conclusions.2) A revised version of the manuscript should be able to distinguish between reinforcement and arousal, since the reinforcements used in this study may cause arousalThank you for raising this important point as it is indeed an important confound for the study of reinforcement-related signals. We had addressed this question in Figure 3A-E and related Figure 3—figure supplement 1A-C. Briefly, we showed that while arousal (as measured by pupil dilation) or locomotion influence the activity of VIP interneurons, the reinforcement response from VIP interneurons is maintained independently of the arousal or locomotion states of the animals during the different epochs of the task (baseline or reinforcement delivery period). To further confirm our findings, we are now adding a new supplementary figure  showing a subset of trials in which the animals were in high arousal states  prior to the start of the trial, and for which the reinforcement delivery did not trigger any additional elevation in arousal. For these trials, the VIP interneuron activity still increased upon reward delivery.In addition, we also present a generalized linear model  to quantitatively estimate the contributions of arousal, locomotion, and reinforcement on the VIP neuronal activity in a systematic way. The model showed that arousal does indeed contribute to the variance in VIP signal as previously reported. However, this systematic analysis also highlighted our new finding that VIP-IN activity reports reinforcement delivery. We hope that this can further strengthen our demonstration that reinforcement-related activity of VIP cells is not just a reflection of fluctuations in locomotion and arousal.3) The manuscript should assess whether well-trained animals display large responsiveness of VIP neurons to reward or reduced responses.We agree with the editor and the reviewers on this important point. Many studies have started to address directly or indirectly the effect of learning on reinforcer-related responses of VIP interneurons . However, the overall goal of this paper is to focus on primary reinforcers (reward and punishment) to demonstrate that they globally recruit VIP interneurons. Now we emphasize this also in the abstract. Nevertheless, it was already evident that during this process the amplitude of the cue-associated responses are proportional with the hit rate: that was the reason why we added Figure 2—figure supplement 2D, which shows that the cue-associated component dominates over the reward-associated component in better-trained mice.4) The authors should revise their interpretation of the data based on the current view of cortical VIP+ cells as a diverse population of neurons that mediate both inhibition and dis-inhibition.We thank the editor and the reviewers for drawing our attention to this important topic. According to the question of Reviewer #1, we analyzed the distribution of the diameter of VIP somata. We have found that both at the level of the entire population and individual mice neuronal somata formed two separate distributions , possibly indicating two populations (CR+ and CR−) of VIP cells shown in a previous study .As we did not find any significant differences in the cell diameter of neurons which were responsive or non-responsive to reinforcement , we concluded that different subtypes of VIP interneurons are similarly activated by reinforcers, but the response variability characterized by the PCA  can indeed reflect subtype-specific differences. We have strengthened the discussion about the complex nature of the VIP cells concerning inhibition and disinhibition in the local circuitry.5) Data analysis and representation should improve according to reviewers comments – see for example comments of reviewer 3 to submitted figures.We thank the reviewers and the editor for raising these imprecisions in the description of some of the methods, especially the TCA. We have improved the methods and the figures according to the reviewers’ comments.Reviewer #1 (Recommendations for the authors):1. The authors need to consider the current view of cortical VIP+ cells as of providers of both inhibition AND disinhibition . In fact, VIP+ cells are highly heterogeneous, and their diversity and functional role across different cortical areas have been highlighted . This is especially important given functional heterogeneity in the response profiles of VIP+ cells observed in the current study across cortical regions. With the help of PCA and k-means clustering method, recorded VIP interneurons were categorized into 5 groups based on their temporal dynamics. These cells possibly belong to different subtypes of VIP interneurons. Post-hoc identification of these neurons using IHC would possibly provide more information on whether the 5 groups of neurons belong to distinct subtypes of VIP cells and ultimately if there are any differences in the distribution of VIP subtypes across depth.This is a very good comment and an extremely important point. As pointed out by the reviewer, the characterization of VIP interneuron subtypes is a very active field with on-going identification of functional, connectivity, and genetic diversity. Although we are not able to perform the IHC the reviewer suggested, we have tried to address the subtype diversity using soma size as a proxy . In Figure 2—figure supplement 3, we found that VIP cells can indeed be separated into two groups on the basis of cell diameter: however, these groups did not differ in their responsiveness to reinforcers . But the PCA clusters indeed showed significant variability: VIP cells with larger diameters were more likely to belong to fast and sustained cluster profiles. One important observation from the cluster analysis is that virtually all clusters show a fast response to reward, but were instead coined mostly based on their late response. These differences in late responses could represent VIP subtype differences with different receptor subtypes, or different integration into the local cortical microcircuit. We have added this new information to the Results section and strengthened the discussion regarding the VIP interneuron subtypes; we also mention the inhibitory effect of the VIP cells.We now say in the discussion:“… An analysis performed to address cell-type diversity using diameter as a proxy separated the cells into two populations, maybe reflecting the CR+/− classification . Given the high proportion of VIP neurons responding to reward and punishment, it seems likely that multiple subtypes of VIP interneurons respond to reinforcers. A potential subtype--heterogeneity manifested itself in the late response phase. Further studies using inter-sectional targeting strategies will be required to provide insight for the potential cell-type-specific origins of the heterogeneity in reinforcement responses.”+ VIP cells  are known to primarily innervate them , at the functional level, reinforcer-induced activation of VIP cells, as a population is likely to produce a net disinhibitory effect  and thereby gain modulation  through changing the balance of inhibition across the somato-dendritic axis .”“Although a substantial proportion of all VIP cell’ boutons directly target excitatory neurons , and the CCK“… As VIP neuron subtypes can have diversified functional roles in the microcircuit beside disinhibition , future research should also focus on these sophisticated circuit operations.”2. Was there a small fraction of VIP+ cells activated by all tasks ?Figure 2C shows that 91 neurons were activated during all 4 trial types. This was, however, not described in the previous version of the manuscript. We now say in the Results section:“…12% of the cells were activated in all trial types.”Not sure, why the AVG response/area is shown. As different cell types were sampled, the best would be to arrange them by depth/layer using the full advantage of the fast volumetric imaging .We thank Reviewer #1 for the constructive idea. We wanted to emphasize in Figure 1C the high response ratio of the VIP interneuronal population in Hit and FA, this was the reason why we ordered the responses according to their maximum amplitude. Our analysis showed that responsiveness does not depend on subtype specificity, therefore we think that it is still rational to depict the transients of the entire VIP population together. But we agree that the suggested depth-dependent arrangement could be informative so we have rearranged the transients by depth and added the new panels to Figure 1—figure supplement 1B.Also, cells could be arranged by their soma size given that CR+/VIP+ have a smaller soma when compared to VIP basket cells .Similarly to the previous point, this is an excellent idea. We have added the figure panel with the aforementioned arrangement to Figure 1—figure supplement 1B.3. TCA allowed authors to understand the variability in the activity dynamics due to inter-trial variability. The interpretation of the obtained results should be added to the main text  for better understanding.We thank the reviewer for pointing this. We have added the interpretation of the TCA into the Results section:“TCA allowed us to quantify, on the one hand, the brain-wide recruitment of VIP interneurons by reward- and punishment-associated responses and, on the other hand, brain region-dependent heterogeneity in dynamics  in an unbiased manner”4. VIP interneurons from all regions under investigation but the auditory cortex showed arousal and locomotion modulation of reinforcement-mediated recruitment of VIP interneurons. The possible reasons need to be discussed in the main text. Similarly, the absence of arousal (pupil diameter change based) modulation of reinforcement response for Hit & FA trials  requires explanation.Thank you for raising this point. First, we should note that the effects of running do not seem to be mediated by the VIP network in this cortical region , which agrees with our findings. We are uncertain about why VIP cells in the auditory cortex show less arousal modulation than those in other brain areas, but there is one mechanism that could possibly explain our findings. Similarly to lateral inhibition in V1 by SOM neurons, there is a tone-related network suppression in the auditory cortex . Interestingly, the intensity of the suppression depends on the arousal: it is weaker at higher arousal levels. As we mentioned in the supplementary information, when the baseline pupil diameter was small, the increase in pupil diameter tended to be higher, and vice versa . It means that in trials with a high pupil change the baseline arousal was generally lower. Indeed, in Hit and FA the average peak amplitude of the low baseline pupil group was 1.9-fold larger than in the high baseline pupil group .Therefore, we can say that in the high pupil change trials, the network suppression could have a large effect, meaning that the SOM cells could successfully inhibit VIP activity after the tone onset, therefore the arousal change related activity could be reduced.The reason why the arousal change modulation is still present in Miss and CR trials  is that Importantly, the difference between low and high arousal change was one magnitude larger in the case of Miss & CR (considering the peaks: 14-fold) than for Hit & FA (2-fold).Summarizing the aforementioned factors, the tone-related network suppression could be accountable for reduced arousal change modulation in the case of Hit & FA, but the difference between low and high pupil change groups was so high in Miss & CR that the network suppression – that was already weaker in Miss & CR – could not overwhelm this modulation in these trial types. We have strengthened our discussion regarding this point:“The lack of modulation by arousal change in the auditory cortex can be explained by the arousal-level-dependent, tone-induced network suppression , a mechanism that in a similar way to lateral inhibition in V1 is controlled by SOM interneurons . High pupil change trials are usually associated with low baseline arousal and thereby with a high level of network suppression, reducing the activity of the VIP cells that is connected to pupil change and, as a consequence, diminishing pupil change – VIP cell activity correlation. As the connection between high pupil change and low baseline pupil was weaker in Miss & CR trials – and also the difference of high vs. low pupil change was higher than in Hit & FA  – the effect of the network suppression did not prevail in these trial types.”5. Fiber photometry recordings from the auditory cortex  reveal higher calcium transients for Hit and FA compared to CR and Miss trials, however, the data from Figure S4A shows higher ΔF/F (%) for Miss & CR compared to Hit & FA. Whether the results in Figure 2A and Figure S4A were obtained from different sets of mice should be mentioned.The data were obtained from the same mice. Unfortunately, in Figure 2A the baseline fluorescence values were calculated in the wrong time interval in this particular case, which shifted the calcium responses: we have now corrected this.Indeed, although the peak of average FA signal was higher than Miss & CR signals, the average ΔF/F (%) for Miss & CR was larger during the 0-2 s interval than for Hit & FA. This time window was chosen for analysis in order to capture the slow time course of the calcium signals.6. Authors conclude in the Discussion section that – only the late response phase showed a correlation with the pupil size, whereas the initial, transient phase followed more closely the reinforcer delivery, however in the Figure 3 D-E, ΔF/F (%) for Hit & FA from high vs low pupil size change trials in the late response phase (~2-3 sec from the outcome) show roughly similar responses . The clarification of how the initial and late response phase was defined is necessary for better understanding. A graph comparing the responses (ΔF/F) of VIP interneurons for high vs low pupil size change trials in initial vs late phase would clarify the extent of the influence of arousal on reinforcement-mediated recruitment of VIP interneurons. Moreover, comparison of ΔP/P in Figure 3C and Figure S4A reveals that reinforcement stimuli trials (FA & Hit) lead to higher pupil size change compared to Miss & CR trials. Considering the modulation of VIP recruitment due to arousal (pupil size change), the reinforcement-mediated recruitment of VIP interneurons might be strongly influenced by arousal. More clarification is needed to conclude about reinforcement-mediated VIP recruitment.If we complete Figure 3C with a bar graph showing average ∆P/P (%) in high and low arousal change in Hit & FA together, and we compare these bars with the calcium responses Figure 3D, we can see that the differences between the high and low arousal groups are not proportional , suggesting that a large part of the reinforcement-associated response below the dashed blue line is independent of arousal.We performed multiple analyses to strengthen this part of the manuscript. As suggested, we have added a panel comparing the difference between high and low arousal-related calcium signal in the initial (0-1 s after reinforcement) and late (2-3 s after reinforcement) response phase: these show an approximately 2-fold larger difference in the late response phase . We have rephrased our conclusion in the Discussion section accordingly.We now say in the results:“This modulation was stronger in the late response phase .”We now say in the discussion:“One caveat of comparing reinforcement-evoked responses to arousal modulation is that the delivery of water reward and air puff punishment usually also drives additional changes in arousal, leading to pupil dilation and/or locomotion. However, the relative change in pupil diameter between the small and large dilation groups was larger than the relative change in the corresponding average reinforcement responses indicating that reinforcement-associated change has a large, arousal independent component . Reinforcement also drove VIP activity when additional pupil dilation and/or locomotion was not detectable. Nevertheless, we found that VIP interneuron recruitment by reinforcers was correlated with pupil dilation, similar to previously-documented arousal modulation . The late response phase showed a stronger correlation with the pupil size, whereas the initial, transient phase followed the reinforcer delivery more closely .”Finally, we estimated the distinct contributions of arousal, locomotion, and reinforcement in a systematic way with the help of a generalized linear model , which revealed the high relative contribution of the reinforcement to the VIP activity.7. In the Discussion section (line 440-441), the authors claim that the VIP activity modulation due to locomotion is weak, but the comparison of activity change due to arousal  and locomotion  shows that the ΔF/F (%) values in all brain regions for arousal and locomotion modulation are comparable. The claim of weak locomotion modulation of VIP activity is not supported by the results. Also, speed modulation was assessed at a very low animal speed . Have authors tried to look at higher speed locomotion, which has been used in the previous studies?Our major goal was not to assess VIP activity with locomotion per se, but rather to show that reinforcement signals are impacted, but couldn’t be explained, by locomotion – see for example Figure 3F and G low speed-change FA traces, where the average speed change is 0 or slightly negative , yet the reinforcement-related activity remained intact. We inserted Results produced by the generalized linear model also supported our original claims . Our experience, both in Budapest and Cold Spring Harbor, was that the VIP-cre animals are really calm and didn’t run much, especially compared to PV-cre or Thy1-cre mouse lines. Given this, and as our primary focus was not to measure locomotion correlation, we haven’t tested higher speeds.In the Results section we now say:“However, the correlation was weaker at the single-cell level (median correlation coefficient 0.11).”In the Discussion section we now say:“…our behavioral paradigm did not encourage mice to run, and their small movements produced only weak modulation in VIP activity  when correlation coefficients were calculated over a long temporal interval. But modulation was similar when restricted to the reinforcement time interval .”8. The authors need to discuss the potential mechanisms of co-activation of VIP+ cells (lines 169-173).We have added a possible explanation to the discussion:“Cholinergic activity in the neocortex was necessary for learning and modulated VIP cells during locomotor activity , also potentially being responsible for the co-activation in the auditory discrimination task.”Reviewer #2 (Recommendations for the authors):– At least for one area (e.g. MPta) the interindividual variance of VIP+ interneuron activity to the different trials and responses has to be provided. This would importantly add to the understanding of the sources of heterogeneity in the recruitment of VIP+ interneurons.The inter-individual variance of VIP+ interneuron activity of FA and Hit responses is shown in Figure 2—figure supplement 1A but with no detailed analysis.Therefore, following the reviewer’s recommendation, we performed further analysis and found that, in line with our PCA results, reinforcement response variability  was not primarily attributable to inter-individual variability as it was ~2.6-fold lower than cell-to-cell variability (in Hit and FA). However, in Miss and CR the CV values were surprisingly similar. We explain these findings in the Results section and have added a new panel to Figure 2—figure supplement 2:“Beyond cell-to-cell and inter-trial variability, inter-individual  variability can also contribute to the heterogeneity experienced in the responses. Therefore, we calculated the coefficient of variation (CV=SD/mean) of the peak of responses among the neurons of individual recordings (cell-to-cell) and compared the result to the CV calculated using the average peak amplitudes of the sessions . We conducted this analysis in parietal cortical measurements  separately in the four trial types . The cell-to-cell CV was comparable among the trial types, Hit and FA being slightly less variable . The inter-individual variability in Miss and CR was similar in magnitude to the cell-to-cell variability, but in Hit and FA it was, surprisingly, ~2.6-fold lower . This means that while the response to tone (without reinforcement) was as variable at the level of individual mice as at the level of individual cells, the reinforcement-related response was less variable across animals, and differences rather came from the cellular level. This latter statement is in good agreement with the PCA results, where the separation did not happen according to the imaging section .”– The stability of the VIP+ interneuron response to Hit and FA across trials should be shown.We performed an analysis to check the stability of the VIP interneuron response. We define stability as a portion of active neurons in a trial. As shown in the figure below, although there was some fluctuation, the stability was maintained across trials. We added this panel to Figure 2—figure supplement 1.Reviewer #3 (Recommendations for the authors):A discrepancy exists with published data that do not show large responsiveness of VIP neurons to reward in visual and barrel cortices. The authors hypothesize that VIP neurons might only respond to reward and punishment during learning. This is the reason they use mice in the phase of task acquisition, that still have "enough" FA trials, while the published studies measure VIP activity in overtrained mice. To solidify this suspicion, the authors could show that, once their mice have fully mastered the task, the reinforcer responses in VIP cells are in fact reduced or absent. This could harmonize their results with these published studies and significantly strengthen the paper.This is a great point and actually true. We are working on a second manuscript addressing that in detail – see response to editor above.tone is larger when the hit rate is higher, and the hit rate in our best-performing animals (70-79%) was just slightly lower than that found by Khan et al., 2018, Sachidhanandam et al., 2016 . We have added a sentence to the corresponding part of the discussion for clarification:However, in our opinion, data in Figure 2—figure supplement 2D already support this view: we show there and in the connected linear regression analysis the relative size of the average cue response compared to the average reinforcement response as a reference (as described in the methods), as a function of the hit rate, which is a behavioral performance parameter. ∆F/F“Those studies, however, used over trained animals for which little to no punishment was delivered and reward delivery was fully predictable. One study limited to the amygdala indeed showed that reinforcement recruits VIP neurons in a time-limited manner . In line with that, in our measurements from well-performing animals, the cue component dominated the signal .”Figure 1: Why are the averages in Figure 1 C and D not corresponding? From the legend it appears all curves should be the averages of the above shown 120 VIP cells. Yet their relative amplitude, as well as time course is definitely different. The hit-curve in 1C seems to start to rise before reward delivery, yet this does not seem to be the case in 1D. In 1D it is unclear what the CR and miss trials are aligned to. Is there really error bars on the bottom plots in 1C as mentioned in the legend?Unfortunately, the legend of Figure 1 was not specific enough. Figure 1C top shows individual trials for Miss, CR, Hit, and FA from the 120 VIP neurons recorded simultaneously. The bottom 4 traces are the corresponding averaged activity of the 120 cells during the 4 individual trials. In contrast, Figure 1D indicates averages of 120 cells and 128 trials (session averages). We have now clarified this information in the legend. This also explains why the mean ± SEM values are not visible: mean, mean ± SEM transients overlap at this high n number . We use thinner lines now and a darker shade of gray, therefore SEM values are more easily distinguished. We have also magnified the traces and aligned the baseline. In panel D Miss and CR traces were aligned to the expected onset of the reinforcement delivery, calculated as the sum of the time between triggering and receiving the reinforcement (fixed 0.5 s) and the time elapsed between stimulus onset and the lick that triggered reinforcement (reaction time). We used the same method to align Miss & CR trials in Figure 2A.In Figure 1D legend we now say:“Left, average transients of a measurement session  for Hit (green), FA (red), Miss (dark blue), and CR (light blue) responses recorded from the 120 VIP interneurons. Gray triangle marks the reinforcement onset in case of Hit and FA. Averages of Miss and CR trials were aligned according to the expected reinforcement delivery calculated based on the average reaction time. Right, average synchronicity (mean ± SEM) and trial-to-trial repeatability (reliability) of individual neuronal responses.”Was MPta chosen for this figure simply because it yielded the biggest n? In Figure 2 it appears to almost be an outlier of all the imaged areas, as it is one of very few areas where VIP neurons appear to entirely lack sensory responses.The cortical location of the imaging site did not significantly affect the size of the cue response. For example, in our other Mpta recording, VIP cells were recruited mainly by the cue onset . Consistently, we did not find any area-specific modulation of the sensory response within the dorsal cortex , the hit rate is a better determining factor as it can cause ~5-fold change in the amplitude of the tone-associated component of the response. For better visibility we have made the arrowheads in Figure 2A smaller and used thinner traces to show the cue and reinforcement components more clearly.However, it is true that we selected this particular Mpta measurement because of the high cell number and the high SNR, as it can demonstrate the main advantages of our random-access scanning system, recording sparsely-labeled interneuron populations from a large volume with good temporal and spatial resolution. As in Table S1 we made a theoretical comparison between imaging systems with different operation principles: it looked reasonable to represent this technical aspect on the first figure too.Figure 2: In panel A it is exceedingly hard to distinguish the thin lines from the same color thick lines and thus see potential sensory responses in any of the areas, perhaps one could change the hue slightly or use the different shade of blue as in panel H? In general, a different color scheme could be considered for accessibility reasons. And a legend should be added.For the speaker sign at the bottom of B it is unclear where the start of the auditory cue is, and if it is the same for all the lines of a plot.We thank the reviewer for the constructive comment. We have now used a different color code for Miss and CR and have modified the legend accordingly. Regarding the auditory cue, it is important to note that FA- and Hit-associated transients were aligned to reinforcement onset before averaging. Therefore, the cue onset time could vary from trial-to-trial covering a narrow interval . To clarify the interpretation, we have added this information to the figure legends. In addition, we now use thinner lines for Hit and FA for better visibility.The time courses here, as well as the ones in F make it seem as though in this data set there was a fixed interval between cue presentation and reinforcer delivery. Or is it similar to Pi et al., where the animals triggered the reinforcer by licking. This is not mentioned anywhere.In this aspect our task was identical to the one used in Pi et al., 2013: the animal had to lick to receive reward, thus the interval between tone and reinforcement onset varied from trial to trial .Accordingly, in Figure 2A, speakers indicate the average and in Figure 2B the approximate time of tone onset in the Hit and FA trials. However, there was a difference between the 2-photon and the photometry setups: for photometry, the tone stopped immediately after the lick that triggered reinforcement. We have now added this information to the methods section and the figure legend.In the methods section we now say:“The absence of licking in go trials was not rewarded . If the animal correctly withheld licking to no-go tones , the air puff was omitted. The time interval between tone and reinforcement onset was a function of the reaction time of the animal. In the figures, responses were aligned to reinforcement onset before averaging in FA and Hit which resulted in a jitter in cue onset time. Reinforcement came 0.5 s after it was triggered, except for during the fiber photometry recordings where reinforcers were delivered upon licking (see below).”“The photometry experiment used the updated version of Bpod that allows the tones to stop immediately when a mouse licks during the tone. Therefore, the duration of tone in the Hit and FA trials varied. Note that when the behavioral task was set up for the two-photon experiment, this function was not available in Bpod and the tone duration was fixed to 0.5 s for all trial types.”In Figure 2F, we applied PCA on the period from 0-4 s after reward delivery (see methods) in Hit trials to quantify sources of variance of the reward-related response. As the tone response occurred before this period, this component appears to be more homogenized across the clusters, especially because these clusters reflected cell-to-cell rather than animal-to-animal variance . In addition, Figure 2F contains only data from cells that were significantly responsive in Hit trials, therefore the heterogeneity is already reduced compared to Figure 2A, as non-responsive cells are not depicted.One session in M1 (hits), as well as one session in M2 (FAs), VIP neurons seem to respond before the onset of the auditory stimulus (judging from the other areas time courses), why is this?As we explained in the previous point, FA and Hit responses were aligned to the reinforcement onset time. The small jitter (~100 ms SEM) in the reaction time blurred the rise of the cue-associated component during averaging which resulted in a slowly rising, ramp-like component. Therefore the speaker sign in Figure 2B indicates the approximate, and not the exact, onset of the auditory stimulus. In the case of the M1 and M2 measurements referred to, both reaction time and jitter of the reaction time was ordinary, but a small positive trend was observed in the baseline period that resulted in a relatively large color change with the previous improper color settings, which we have now corrected.All abbreviations should be explained in the legend Corrected.Figure 3 C,D,E: why were these areas chosen, or which areas are included in each of these plots? Do differences between hits and FAs exist? There appear to be potentially interesting areal differences.We selected mPFC, SS, and Mtr, because we wanted to choose regions which are relevant for our behavioral experiments. Measuring ACx is evident, as the cue was a tone, mPFC is involved in task-dependent choices and reward representation, the motor cortex is required for the motor reaction; we also added the somatosensory cortex, as it is also inevitably activated by tactile sensory inputs during the task.Figure 3C represents the average pupil change during the measurements from the motor and somatosensory regions . The corresponding pupil traces of Figure 3E (mPFC) can be found in Figure 3—figure supplement 1A:A. Average pupil dilation traces during high (red and orange) and low (black and gray) pupil changes for FA and Hit trials for SS and Mtr recordings in panel D.B. Population averages for Hit and FA during high and low pupil change in the SS and Mtr regions. Bars indicate average peak amplitudes . Even in the late period, when the outcome responses were dissipated, larger changes in pupil diameter at the time of reinforcement were associated with higher VIP responses.C. Same as D but for fiber photometry in the mPFC. Corresponding pupil dilation traces can be found in Figure 3—figure supplement 1A.Indeed, we observed some trial type-specific differences , but our general observation was the presence of modulation across the cortex, except for the ACx.In the figures of the time courses of the ACx repsonses (2A and S1D) it seems like VIP cells only have sensory responses in miss and CR trials. Why is this bump absent from hit and FA trials?In our task design in the photometry experiment, if a mouse licked during go or no-go tones, the tones stopped immediately and a reward or punishment was delivered. The reaction time of mice in the Hit and FA trials were 0.35 ± 0.147 s and 0.84 ± 0.108 s, respectively. Considering the slow response time of GCaMP  and photometry collecting population signals, it is a short time window for a bump to be developed. After a discussion and feedback from our colleagues, we decided to align the calcium traces of Miss and CR to the mean reaction time and added an explanation to the legend of Figure S1D, which is now Figure 1—figure supplement 1E. Traces in Figure 2A were also adjusted accordingly. We hope this alleviates any potential confusion for readers.line 227: How are these 606 reward delivery sensitive cell related to the 622 in Figure 2?We focused on reaching low reconstruction error, so removed 16 cells with lower SNR that would have disproportionately increased our error. We added this information to the methods section:“… 16 cells were excluded from this analysis as they disproportionately increased the reconstruction error.”For the arousal and running modulation: why is the data split here and no correlation calculated as for the relationships to behavioral performance and sensory tuning?We reasoned that splitting the data by the median value of pupil change / baseline pupil diameter and speed change could better highlight the modulatory effects of the arousal and locomotion compared to showing correlations only. However, we calculated correlation between cell activity and pupil size and the median value was 0.31 . We also calculated the correlation with the locomotion speed, and we added the median value (0.11) to the corresponding section (see line ~397)The running paragraph: it needs to be clarified whether they look at running speed per se, as much of the paragraph is written (fast and slow running conditions), or at speed changes, as the beginning of the paragraph states and the values (partly negative) would make it seem. If it is indeed speed change, they should rename the conditions.We thank the reviewer for the helpful comment. Indeed, we show speed changes (∆speed) in Figure 3 and Figure 3—figure supplement 1. We replaced fast and slow running phrases with high vs. low speed-change. We describe how speed change was calculated in the methods:“Velocity traces were first Gauss filtered. They show the absolute speed of the movement, regardless of the direction. In Hit and FA trials, we defined the change in the running speed as the speed difference between the reinforcer delivery time period  and baseline time period . In Miss and CR trials, the speed difference was calculated between the tone delivery time period  and baseline time period . Trials were separated into low and high speed-change groups according to the median speed-change value.”Figure S2F seems very important. Was also uncued negative reinforcement presented? How does the uncued data compare to the cued?Uncued air puff wasn’t presented during the auditory discrimination task, however we collected data from a Pavlovian task, in which both cued and uncued reward were presented. We compared the responses in Is S5A the exact same data as in 4D, just plotted in a different way? A statistical test should be performed to judge the significance of this correlation.Yes, the two plots represent the same data. The correlation of the reinforcement- and visual stimulation-related responses turned out to be significant. We thank the reviewer for pointing this out. This correlation could come from the differences in general excitability.The methods need to be improved. As is there is not enough detail and analyses cannot be understood properly. For example: What does smoothing according to the reaction time periods mean? Reaction time periods are not even mentioned in the description of the task. Please explain time-wrapping. Why is 1.5s 30 data points when the sampling rate is 27.7 Hz and not 20Hz? Why are only 10 trials selected? Why the first? Why is K then 40?We have now improved the TCA-related methods section according to the reviewer’s comment. As for the specific points raised by the reviewer:1. The single-trial traces were smooth using the MATLAB smooth function2. The sampling rate of our AOD-2p recordings varied depending on the number of cells being recorded. For the TCA, all trials were aligned to the start of the cue delivery. However, because of the varying sampling rate, the subsequent delivery of the outcome (Hit and FA) does not align across sessions. The jitter in reaction time also resulted in variance of the recorded frames across trials (see more about the reaction time in the “Auditory discrimination task” section of methods). Applying TCA or any type of clustering without fixing these sampling rate and reaction time related jitters could lead to a classification according to the sampling rate and not to the trial types or other task-related event. Thus, we decided to mitigate this risk by ‘time-warping’ the period corresponding to the cue delivery as described in Williams et al., 2018 .3. The number of trials of each type varies across sessions. To build an N×K×T matrix where N is the number of neurons coming from different sessions, K the trial numbers  and T the time, we had to select the same number of trials for each neuron coming from different sessions. We used 10 trials per trial type per session/neuron as it allowed us to include all sessions  while maintaining an equal representation of all trial types. Our rationale for using the first 10 trials of each trial type was that it would limit the variability in the animal states. However, we did try using 10 trials per trial type at random and observed the same effect.We now say in the discussion:2+ responses recorded during the discrimination task. After smoothing, single-trial neural activities corresponding to the reaction time periods for Hit and FA trials were time-wrapped to a fixed 1.5 s / 30 data points in length. This step was necessary because: 1, the reaction time varied from trial to trial; and 2, the variable number of neurons recorded in the different sessions led to a variable sampling rate for imaging. These factors led to a trial- and session-dependent number of images between the cue and the reward delivery. All recordings were rendered non-negative by subtracting the minimal fluorescence ΔF/F value for each cell. Data were finally normalized by dividing by the average maximum fluorescence ΔF/F value of Hit-only trials. Only the first 10 trials of each type were then selected for each cell and each session and assembled in an N×T×K matrix where N=771 neurons, T=time (s), K=40 trials. TCA reconstruction error was computed with different latent numbers  with 10 different initial conditions for each latent number. Using 3 latents led to a reconstruction error of 21%.”“We applied Tensor Component Analysis (TCA)  on somatic caSupplementary figure S3C is never mentioned.Corrected. We mention it now in the section about the TCA.“…After preprocessing  we used non-negative tensor decomposition  and focused our analysis on rank 3 TCA…”We thank Reviewer #3 for all the constructive comments.