Offset responses in auditory processing appear after a sound terminates. They arise in neuronal circuits within the peripheral auditory system, but their role in the central auditory system remains unknown. Here, we ask what the behavioral relevance of cortical offset responses is and what circuit mechanisms drive them. At the perceptual level, our results reveal that experimentally minimizing auditory cortical offset responses decreases the mouse performance to detect sound termination, assigning a behavioral role to offset responses. By combining in vivo electrophysiology in the auditory cortex and thalamus of awake mice, we also demonstrate that cortical offset responses are not only inherited from the periphery but also amplified and generated de novo. Finally, we show that offset responses code more than silence, including relevant changes in sound trajectories. Together, our results reveal the importance of cortical offset responses in encoding sound termination and detecting changes within temporally discontinuous sounds crucial for speech and vocalization. Offset-responsive neurons are present through the whole auditory pathway starting from the cochlear nucleus CN;  to the sThe perceptual significance of offset responses has been difficult to assess. They have been postulated to play a role in sound duration selectivity , in gap Compared to onset responses, offset responses in the central auditory pathway are typically less prevalent . They arWe combined behavioral experiments with optogenetics, electrophysiological recordings, and antidromic stimulation to better understand the role of cortical offset responses in sound perception and the properties that distinguish them from the subcortical ones. Our results reveal that AAF is highly specialized for processing information on sound termination and that minimizing its offset responses decreases the mouse performance to detect when a sound ends. By studying the influence of different sound parameters on AAF and MGB offset responses, we demonstrate that cortical offsets are inherited, amplified, and sometimes even generated de novo. First, we found that AAF, unlike MGB, shows a significant increase in offset response amplitudes with sound duration. Then, we report that white noise (WN) bursts – unlike pure tones – only evoke offset responses in AAF but not in MGB. Finally, we show that offset responses are present in AAF whenever a frequency component is removed from multifrequency stimuli and therefore may have a further role than solely coding silence.Taken together, our findings suggest a particular involvement of AAF offset responses in sound termination processing and point to the importance of this cortical subfield for advanced processing such as tracking sound duration or detecting changes in frequency and level within temporally discontinuous sounds.The perceptual significance of cortical offset responses has been difficult to assess. Indeed, confounds about perceiving a sound and its termination are intricately linked. Changing the neuronal activity of sound offset responses to evaluate its contribution in sound termination detection has not been tested. To assess the behavioral relevance of auditory offset responses, we developed a sound termination detection task in which mice expressing channelrhodopsin-2 (ChR2) in parvalbumin-positive (PV+) cells learned to detect the end of 9 kHz pure tones PT; . As, at After this training phase, animals underwent a craniotomy. On the following days, they were tested with tones played at 60 dB SPL. All our behavioral experiments with optogenetic manipulations were coupled with electrophysiological recording. For all behavioral sessions, we first identified AAF based on the tonotopic gradient obtained with electrophysiological recordings. We then placed an optical fiber above AAF (above the dura) and titrated the laser (473 nm) power to remove offset responses. Finally, we performed the behavioral session with these specific optogenetic parameters (optic fiber placement and laser power). We confirmed at the end of each session that offset responses were removed during the laser-on trials. No optical shield was used. In half of the trials (pseudorandomized each day), a laser light (473 nm) was delivered above AAF for 200 ms starting at sound termination  to activρ = 0.71, p < 0.0001, Spearman correlation), suggesting that our protocol allows us to identify actual offset responses. Finally, we asked if the responses at sound termination would influence the onset responses of a tone played shortly after. As offset and onset responses are driven by different sets of synapses . We examined the classifier accuracy for the model trained and tested on the spontaneous, onset, sustained, offset, and late response  from an Knowing that offset responses in AAF are behaviorally relevant and influence sound termination perception, we next asked what mechanisms drive these cortical offset responses and what properties distinguish them from subcortical ones. We performed awake electrophysiological recordings in AAF and MGB and analyzed the response profile dynamics of cells within both regions. We recorded multiunit activity evoked by 50 ms PT with varying frequency (4–48.5 kHz) and sound level (0–80 dB SPL) presented with randomized interstimulus intervals (ISIs) (500–1000 ms).K-Means clustering of spike-sorted unit (SU) activity was used to identify cells with distinct temporal dynamics. The clustering method was performed on the averaged poststimulus time histogram (PSTH) in both MGB  and AAF  recordings pooled together. The analysis time window for the clustering was chosen to emphasize the offset rather than the onset responses . Davies–Bouldin evaluation was used to determine the optimal number of clusters . Other categories were represented in much smaller proportions: onset-only (3.2%), late-onset (6.1%), and sustained (7.5%). In contrast to MGB, no suppressed cells were recorded in AAF. The overrepresentation of onset–offset responses in AAF compared to the preceding nucleus of the auditory pathway indicates that AAF is highly specialized in processing information on sound termination.clusters . Nine clppressed . Few cluectively . Cells wectively . Suppresn = 1548 SU, 5 animals) with antidromic stimulation of AAF, followed by PT stimulation to characterize the temporal dynamics of cells projecting from MGB to AAF  or was already present in the input layer 4 (L4), we compared the dependence of offset responses on sound duration in these layers . Our recAs MGB does not contain fast-spiking (FS) interneurons , we thenTogether, these results reveal an essential difference between AAF and MGB offset encoding and demonstrate a clear amplification of the dependence of offset responses on sound duration in AAF as compared to MGB.Next, we compared offset responses in MGB and AAF evoked by sounds with different spectral complexity. We recorded responses in both regions to 500 ms PT, and WN bursts played at 60 dB SPL . Our results showed very distinct neuronal activity patterns in response to WN or the spectrally less complex PT, both in MGB and in AAF . In MGB,We then asked whether offset responses to WN stimulation differed between different neuronal populations of AAF. We found that responses to WN were significantly more sustained in L4 than in L2/3 . It seemThe PSTHs of AAF neurons responding to PT and WN stimulation indicated that WN evoked more sustained activity than PT . More spStudying offset responses evoked by WN bursts revealed two fascinating differences between MGB and AAF processing. First, offset responses to WN stimulation are not present in MGB but are present in AAF. Second, cells in AAF seem to follow ongoing WN stimulation with a bursting activity happening every ~200 ms.n = 275 SU, 6 animals) and AAF . The complex sound consisted of three frequency components  played at 60 dB SPL, which had a common onset but ended at different time points . The offset responses evoked by removing one or two frequency components demonstrate that neurons can encode the disappearance of a frequency component in an ongoing sound, especially in AAF  knock-in line with C57BL/6J background . This strain drives the expression of Cre in PV+ interneurons of the cortex with the minimal leak. We crossed this line to the Ai32 line (JAX stock number 024109 with C57BL/6 background), which encodes the light-gated depolarizing cation channel channelrhodopsin-2 conjugated to e-YFP after a floxed stop cassette under the CAG promoter. Wild types were C57BL/6J mice . Thirty-nine mice were used in this study. Mice were a mixture of males and females and aged between 7 and 12 weeks of age at the time of behavioral training or electrophysiological recording.All experimental procedures were carried out in accordance with Basel University animal care and use guidelines and were approved by the Veterinary Office of the Canton Basel-Stadt, Switzerland . To target the opsins to PV+ interneurons, we used PV-Cre (Cre was targeted to the 2) was performed with a scalpel just above the right ACx and covered with silicone oil and silicone casting compound  during the 2 hr recovery period from the anesthesia.Awake electrophysiology recordings and behavior experiments were performed on adult (7–12 weeks) male and female C57BL/6J mice . For surgeries, mice were anesthetized with isoflurane , and subcutaneous injection of bupivacaine/lidocaine  was used for analgesia. A custom-made metal head post was fixed with super glue  on the bone on top of the left hemisphere and used to head-fix the animals. Their body temperature was kept at 37°C with a heating pad , and lubricant ophthalmic ointment was applied on both eyes. Craniotomy . Mice were head-fixed and placed in the cardboard tube for recordings inside a soundbox. Extracellular recordings were conducted in AAF  and MGB . Multichannel extracellular electrodes with 32 channels  or 64 channels  were inserted orthogonally to the brain surface with a motorized stereotaxic micromanipulator  at a constant depth . Responses from extracellular recordings were digitized with a 32- or 64-channel recording system  at 24,414 Hz. Sorted units were identified from raw voltage traces using kilosort  at 200 kHz sampling rate and played through a calibrated MF1 speaker  positioned 10 cm from the mouse’s left ear. Stimuli were calibrated with a wide-band ultrasonic acoustic sensor .n = 5 animals). First, mice were anesthetized with an intraperitoneal injection of ketamine/xylazine , and subcutaneous injection of bupivacaine/lidocaine  was used for analgesia. Ketamine (45 mg/kg) was supplemented during surgery as needed. For surgery, mice were head-fixed, and their body temperature was kept at 37°C with a heating pad . Two separate craniotomies (~2 × 2 mm2) were performed with a scalpel above the right MGB and ACx and covered with silicone oil. AAF was mapped with electrophysiology recordings based on the ventrodorsal increase in BF to identify the target area for stimulus pipette insertion. Then both craniotomies were covered with silicone casting compound  during the 2 hr recovery period from the anesthesia. Electric stimulator  was connected to a stimulation isolator , which was then connected to the wire electrode. The wire electrode was fixed in pulled capillary glass (tip: <10 µm) filled with saline and then inserted into AAF (~300 µm). Monophasic square wave pulse was generated with electronic stimulator as pulse train ; intensity: 30 μA . Local analgesia was provided with subcutaneous injection of bupivacaine/lidocaine . A head post and a ground screw were fixed to the skull with dental cement . The portion of the skull above the target recording site was left free from cement and covered with a thick layer of Kwik-Cast Sealant . Postoperative analgesia was provided with an intraperitoneal injection of buprenorphine (0.1 mg/kg). After recovery from the surgery for a couple of days, mice were food restricted. Training. Mice were then placed in the cardboard tube and adapted to the head restraint. The speaker was placed 10 cm away from the left ear of the animals. Next, they were taught to associate a sound offset with reward availability. Mice were trained to detect sound offset of PT (9 kHz) played at 80 dB SPL (training) with varied duration . The rise-ramp of the tones was always fixed to 10.0 ms, while at the offset fast (0.01 ms) or slow (10.0 ms) ramp was used and varied randomly. During the beginning of the training, mice had to lick within 3 s after sound offset to receive a drop of soya milk as a reward, and the trial was considered a correct hit. If the animal did not lick within and after the tone trial was considered as missed. If the mice licked while the tone was ongoing, they received a mild air puff oriented toward the right eye and a time out (2–3 s) until the subsequent trial could start. These trials were removed from the analysis as the target (sound offset) could not be correctly delivered. Sounds were delivered without preceding cues at random ISIs ranging from 3 to 5 s. Licks were detected with a piezo sensor attached to the reward spout. Within consecutive training days, the reward window was shortened down to 1 s. Craniotomy. Once animals performed at least 30% of correct hits, they were considered initially trained and had a craniotomy performed under ketamine/xylazine (80 mg/kg) and AAF mapping on the same day. Recordings. On the following day, mice were moved on to a tasting phase where behavior training was coupled with acute electrophysiology recordings in AAF. During the testing phase, tones were played at 60 dB SPL, and laser (473 nm) was added unilaterally above the right AAF and activated continuously for 200 ms following sound termination in half of the trials (pseudorandomized each day). All experiments were performed in a soundproof box  and monitored from outside the soundbox with a camera . The laser power was set around 4.2 mW and was adjusted every day to cause a robust suppression of offset or sustained responses in PV− cells. The testing phase was carried out for up to 6 days. Behavioral control and data collection were carried out with custom-written programs using a complex auditory processor  and further analyzed with MATLAB .https://doi.org/10.5061/dryad.41ns1rnfg).All data analysis was performed using custom-written MATLAB (2019) (Mathworks) code. Original spike data and code are available on Dryad (To determine BF and tuning receptive fields (TRFs), we used PT  varying in frequency from 4 to 48.5 kHz in 0.1-octave increments and in level from 0 to 80 dB SPL in 5 dB increments. TRFs, best frequency, and spiking rates were calculated in fixed time windows: onset: 6–56 ms, offset: 56–106 ms. TRFs were smoothed with a median filter (4 × 4 sampling window) and thresholded to 0.2 of peak amplitude. Onset and offset BF was defined as the frequency that elicited maximal response across all sound levels. Onset and offset peak latency was determined as the time point in which the smooth PSTH (kernel = hann (9)) collapsed across all tested stimuli showed a maximum response (binning size: 5 ms). Spontaneous activity was calculated based on activity preceding sound onset .To study responses to tones with different durations, we used 10 repetitions of PT  with 4 ms cosine on and 0.01 ms cosine fall-ramps, which were varied in duration , ISI  and played at 60 dB SPL. For AAF, the frequency was fixed to 9 kHz because 9 kHz PT evoked significant offset responses in almost all tested AAF. Offset spike rates were calculated in a fixed time window of 6–56 ms following sound termination.To study offset responses in MGB and AAF evoked by sounds with different spectral complexity, we recorded responses in both regions to 500 ms PT and white WN bursts played at 60 dB SPL with 4 ms cosine on and 0.01 ms cosine fall-ramps . WN bursts were not fixed but consisted of randomly chosen noise samples. Offset spike rates were calculated in a fixed time window of 6–56 ms following sound termination.To study the dependence of onset and offset responses on the temporal profile of a tone, we varied rise- and fall-time at sound onset and offset . PTs  were played at 60 dB SPL for 500 ms and repeated 50 times. The peak amplitude of offset responses was defined in the first 100 ms after stimulus onset or offset.To check if offset responses encode changes within ongoing sound, we used tone consisting of three frequency components  played at 60 dB SPL and repeated 60 times. All frequency components had common onset but were terminated at different time points . Offset spike rates were calculated after every component removal in fixed windows: 306–356 ms; 406–456 ms; 506–556 ms.Both the ODI and the hit rates were used to evaluate the animal’s behavior in the sound termination detection task. ODI was defined as:Hit was calculated as the percent of 1-s long trials in which the mouse licked between 1 and 2 s (during the reward window), Miss as the percent of 1-s long trials in which the mouse did not lick between 1 and 2 s (during the reward window), False Alarm as the percent of 2-s long trials in which the mouse licked between 1 and 2 s (during the last 1 s of sound), and Correct Rejection as the percent of 2-s long trials in which the mouse did not lick between 1 and 2 s (during the last 1 s of sound). Trials with early onset licks (happening within 1 s following sound onset) and trials with 1.5 s long sounds were not taken into account for calculation of ODI. Similarly, trials with licks before sound termination were discarded from the hit rate analysis . Spontaneous activity (50–100 ms before sound onset), onset response (0–50 ms from sound onset), sustained response (500–450 ms before sound offset), offset response (0–50 ms from sound offset), or late response (500–550 ms after sound offset) were used to train and test the model. Logistic regression was implemented using the sklearn function LogisticRegression with the lbfgs solver and L2 regularization to avoid over-fitting. Eightfold cross validation was performed by leaving out a random 12.5% subset of trials to test the classifier performance, and remaining trials were used to train the classifier. A range of regularization values was tested , and the one that gave the smallest error on the validation dataset was chosen as the optimal regularization parameter. The classifier accuracy was computed as the percentage of testing trials in which the animal’s choice was accurately predicted by the classifier and summarized as the average across the 10 repetitions of trial subsampling. The spiking activity of each neuron was z-scored before running the logistic regression model. Trial labels were shuffled to confirm that decoding is not working for random data. This procedure was repeated 10 times. Then the average across the 10 repetitions was used to assess the classifier accuracy for randomized data. To remove all the sessions with a too small number of trials or too few offset cells, only the sessions with a significant difference in classification accuracy between real and shuffled data based on the late response (0.5 s after sound offset) were used.The logistic regression model was used to decode animal performance from neural responses , and numbering at least 14 behavioral sessions or 50 neurons per group, except for the number of putative FS neurons in n = 28). Statistical tests were performed with GraphPad Prism software version 7.03 . The standard error of the mean was calculated to quantify the amount of variation between responses from different populations. PSTHs display (1) mean ± STD if they represent one condition, (2) mean ± standard error of mean if they represent more than one condition on the same panel. A nonparametric, unpaired Mann–Whitney test was used to calculate whether there were any significant differences between medians of recordings in AAF and MGB. Wilcoxon paired test was used to compare differences between paired values obtained in different treatments. Friedman test with multiple comparisons was used to compare many conditions. Two-way ANOVA was used to test the main effects of sound duration and intervals on offset responses and their interaction effect. Dunn’s multiple comparisons test was used to perform multiple pairwise comparisons. Spearman correlation tests were used to test for significant associations between pairs of variables measured with ranking. The effects were named significant if the p value was smaller than 0.05 (*), 0.01 (**), 0.001 (***), or 0.0001 (****), for a confidence interval of 95%, 99%, or 99.9%, respectively.Sample size was determined based on standards established by previous publications studying single-neuron activity with in vivo recording, which have been adequate to demonstrate significant population effects. A traditional power analysis is not possible because noise properties of neural data are difficult to estimate a priori. Based on norms for the field, we acquired data from at least five animals, except for  The work demonstrates specific neurophysiological cortical mechanisms for offset responses that are interesting in themselves. Two referees highlighted issues with the behavioural experiments that have been addressed in the revision. Reviewer #2 makes another minor suggestion that he authors might consider before publication of the final version. In the interests of transparency, eLife publishes the most substantive revision requests and the accompanying author responses.Decision letter after peer review:[Editors’ note: the authors submitted for reconsideration following the decision after peer review. What follows is the decision letter after the first round of review.]eLife. Your article has been reviewed by 3 peer reviewers, including Timothy D Griffiths as the Reviewing Editor and Reviewer #1, and the evaluation has been overseen by a Reviewing Editor and a Senior Editor.Thank you for submitting your work entitled "Emergence and function of cortical offset responses in sound termination detection" for consideration by eLife.We are sorry to say that, after consultation with the reviewers, we have decided that your work will not be considered further for publication by The referees found the work very interesting. The neurophysiological data are excellent, but issues are raised with respect to the behavioral data. After discussion the referees all concur that these require further experiments and could not be addressed on the basis of the current data. For this reason, we have recommended rejection of the manuscript. We would be pleased to consider a resubmission that incorporated the additional data suggested.Reviewer #1:There has been a great deal of recent interest in the neural basis for offset responses given their hypothesised importance to perception. The possible behavioural relevance to cues like sound duration and gap duration has been taken as a self-evident truth in some work. I found this work attractive in actually testing the relevance of offset responses to duration perception in a mouse model in addition to examining the brain basis. The work is thorough and well executed. The work demonstrates offset responses that occurs for the first time in auditory cortex distinct from A1 where prevention of offsets by activating cells causes worsening of behavioural performance.1. I think an initial concern in discussion of this manuscript about artefactual effects of spectral splatter due to abrupt sound termination have been addressed in this version of the manuscript.2. The data appear to support a specialisation for offset response in AAF but have offset responses and their behavioural relevance been examined in A1? The analysis in figure 4 convincingly demonstrates changes in offset responses between MGB and AAF (which is monosynaptic connection) but it would be interesting to know about A1. I appreciate there is s strong prior related to AAF based on previous work but the offset responses being in AAF has been almost taken as a given in manuscript.Reviewer #2:In this study, the authors examine: (1) whether offset responses, where neurons respond upon termination of a stimulus, are behaviorally relevant; (2) whether offset responses are merely inherited from subcortical stations or are generated and amplified in cortex; and (3) whether offset responses simply encode sound termination or if they carry stimulus identity information as well. They show, using a combination of optogenetics and behavior, that suppressing offset responses in auditory field AAF results in an impairment of sound termination detection. They then show, using single- and multi-unit recordings, that the behavioral choice of the animal can be decoded on a trial-by-trial basis from the offset and late response phases. Finally, using antidromic stimulation and using multiple stimuli, the authors show that AAF offset responses are not wholly inherited from the auditory thalamus.The electrophysiological elements of the study seem solid and well-performed. Some weaknesses of the study include the effectiveness of task acquisition by the behavioral subjects, and behavioral analyses that discard trials with potentially useful information. Some statistical tests may not be appropriate and brings into question the results of the decoding analysis. Very recent and highly relevant publications are not discussed in the study. Additional control analyses would strengthen the manuscript.1. Two very recent studies address questions that are central to this manuscript. First, Li H et al.  show using optogenetic manipulations in primary auditory cortex (A1) that A1 OFF activity is required for the perception of sound duration. These results must be discussed in the context of the authors claim that AAF might be specialized for the detection of offset responses.Elife, 2021, 10:e53151) argue for a role of recurrent A1 connectivity in shaping offset responses in cortex, including the fact that the offset response carries information about stimulus type. These results should be discussed in the context of the authors observations as well.2. Second, Bondanelli et al. , could provide a complete picture of the behavior.Considering 30% correct trials as ‘trained’ seems well below traditionally accepted metrics of when a animal is considered trained, especially for a relatively simple detection task. Usually, this number is closer to 70% correct – for example, in the Li et al. 2021 paper mentioned above, mice were considered trained after reaching 90% correct trials on a sound duration discrimination task. Better yet, a d-prime of 1 or 1.5 when false alarms are also considered is a more sensitive metric of behavior .4. In decoding of performance from activity, given that the reward window opens at offset and is open for only 1 s, the inclusion of the ‘late’ phase is problematic unless it can be shown that licks do not occur within 0.5 s of sound offset. This bump for the hits could result from multiple effects – movement, reward, licking sounds etc. The data supporting the claim of better decoding from offset responses hinges on Figure 3c, where offset responses yield greater accuracy than onset responses. However, pairwise Wilcoxon tests do not seem appropriate for these data where multiple comparisons are being made. The authors should use an ANOVA or Kruskal-Wallis test followed by by multiple-comparisons corrected posthoc tests.5. From Figure 1E, it appears that the post-inhibitory rebound in other cells in the laser on condition has a similar magnitude to the offset response in the laser off condition. Could the rebound be driving AAF responses that signal an offset, albeit delayed by about 0.2 s, that the animals could be using to detect sound termination? To answer this question, could the authors analyze both the neurophysiological data, as well as determine if the correct responses in the laser ON condition have longer latencies consistent with this 0.2 s delay?6. If the authors have the data available, it would be great to see a similar control as shown in Figure 2j-m for the longer ramp duration as well in Figure 1. More detail in the methods section as to how the fiber was placed over AAF (in craniotomy but above dura?), whether it was optically shielded to prevent visual cues etc. would be helpful.7. For the onset-offset neurons that do not have a sustained response profile, it is clear that the highly correlated offset is an important distinguishing cue – it provides a high-SNR signal between the offset response and the previous silent period (when the tone is on). But what if  some amount of sustained activity is present? Is offset-detection behavior worse, and is decoding accuracy using the classifier also worse? If behavioral data is not available, could additional analyses be performed to predict sound termination time for pure tones and white noise, and make a prediction as to what would happen behaviorally?Reviewer #3:The goal of this study was to assess the function of cortical offset responses of the anterior auditory field (AAF) in sound perception. The authors used a combination of behavioral, electrophysiological and optogenetic techniques to study the properties of cortical offset responses. Through behavioral experiments combined with optogenetics, the authors first claim to find that inhibiting offset responses in the AAF decrease the mouse’s ability to detect when a sound ends. Furthermore, they report that larger offset responses correlate with an increase in the mouse’s ability to detect sound termination. Functionally, the authors demonstrate via electrophysiological experiments that cortical offset responses have a component that is generated in the AAF and therefore not only inherited from the periphery. The authors also find that offset responses increase with sounds that have longer duration and therefore do not simply encode for silence. The electrophysiological investigation of the properties of cortical offset responses is well designed and the conclusions are justified by the data. However, several questions about the behavioral paradigm arose that warrant further control experiments and re-examining interpretation.1) The behavioral paradigm suffers from a design in which it is difficult to estimate the false alarm rate. Therefore, it is unclear whether the mouse is trained to lick in response to tone offsets, or rather to reduce licking during the sound presentation. The criterion for “fully trained” is set at 30% hit rate, well below chance , which seems somewhat low. It is unclear what the mouse licking is reporting.2) This interpretation of behavioral performance is complicated by the high rate at which mice licked during the trials . (Is the legend for figure S1 correct?) Since the authors report that “Trials with licks at the tone onset were discarded from the analysis ”, question arises whether 60% of the trials were excluded for some mice. In that case, is the lick rate on the trials that were kept simply by chance?3) The optogenetic manipulation of AAF during behavior drives a relatively small (20%) reduction in performance . It is therefore unclear that the conclusion that AAF is necessary for sound offset detection is supported by the data. The behavioral + optogenetic paradigm should be better described in methods, allowing to better assess the presentation of the laser (see point 8).4) The reduction in performance may be due to general laser presentation and not specific to sound offset. It would be useful to present the laser during the sound or at different points in the stimulus to better understand how its presentation relates to offset responses specifically .5) It would be helpful to present more details about how the classifier was trained and tested . The neuronal classifier also does not seem to predict accurately behavior as the accuracy for the offset responses is at ~65 % . It is furthermore interesting that much of behavior can be explained based on spontaneous neuronal activity, and therefore an alternative interpretation would be that cortical state . This also suggests that a control as suggested in point 4 (laser presented at different time points in the stimulus) would be useful, as the classifier would predict significant reduction in performance based on suppression of activity prior to sound, and not at sound offset.eLife. Your revised article has been evaluated by Barbara Shinn-Cunningham (Senior Editor) and a Reviewing Editor.Thank you for resubmitting your work entitled “Emergence and function of cortical offset responses in sound termination detection” for further consideration by eLife makes great efforts to avoid multiple cycles of revision but referee #2 and referee #3 raise further important points in their reviews about the behavioural data that need to be addressed.The manuscript has been improved but there are some remaining issues that need to be addressed, as outlined below. Reviewer #2:I appreciate the authors’ detailed responses to the concerns arising from the earlier review of this manuscript. There are no major concerns with the electrophysiology part of the revised manuscript.Behavior: My concerns with behavioral experiments and what the mice are actually reporting in the task remain.1) I respectfully disagree with the authors’ statement that false alarm rates cannot be calculated for detection tasks. The term ‘False Alarm’ is derived from signal detection theory, where the subject reports that a signal was present when a signal was not actually present. In this case, the signal is the offset. If a mouse reports an offset by licking during the sound , that outcome should be a false alarm. In the authors’ task design, only correct rejections cannot be specified . I agree with the authors that if the mouse starts licking before even the sound onset, those trials should be discarded.2) “Likewise, unlike in a discrimination task, a hit rate of 50% is not a chance level in a detection task.”– It is not a question of what the chance level was, but rather a question of whether the mouse was reliably detecting the offset. P(Hit) + p(FA) + p(Miss) + p(CR) = 1, and a measure of performance would take into account the ratio of desirable (p(Hit)+p(CR)) and undesirable (p(FA)+p(Miss)) outcomes. To illustrate, in Figure S2 panel h, I count 50 trials, with n(Hit) = 23, n(Miss) = 11, nFA = 16, and n(CR) = 0. In this counting, the number of desirable outcomes (23+0=23) is lesser than the number of undesirable outcomes (11+16 = 27) and would result in a d’ = z(Hits) – z(FA) = 0.37. However, if trials in which animals licked during the sound (which I coded as FA) are dropped, then n = 50-16 = 34, with n(Hits) = 23 (23/34 = 67%). This is why dropping the onset lick trials and considering 30% hits as reaching criterion for training is not convincing.Other metrics to better capture the behavior might be a discrimination index as used in, for example, Schwartz and David, 2018. Here the cumulative lick probability can be used to calculate ROC curves.3) Page 5, lines 121 and 125: The authors use a very similar effect size change in hit rate for PV-Cre animals (4.5 +/- 2%) and for wild type animals (4.4 +/- 2.4%) to make the claim that there was a significant change in hit rate (p = 0.0264) for PV-cre animals, but no change (p=0.058) for the wild type animals . The appropriate comparison in this case would be between 1H and 1L. . It would be instructive if the authors could plot the Laser OFF and ON hit rates separately for the two groups  so that one can also appreciate if there are overall differences in performance levels.Additionally, the sizes of the error bars in Figure 1H and 1L are inconsistent with the numbers reported ?Citing recent literature:I appreciate the authors’ inclusion of two recent studies in the Discussion section. However, these studies also affect the framing of the manuscript in the introduction section.Page 2, line 39: “De-novo generation or amplification of offset responses in these areas have not been demonstrated yet.” – Please rephrase and appropriately cite the Bondanelli 2021 study here.Page 2, lines 42 – 52: In this paragraph discussing the perceptual significance of offset responses, please appropriately discuss the Li 2021 study. Please rephrase the last sentence of this paragraph.Page 4, line 86: Please rephrase “Changing the neuronal activity of sound offset responses without changing any other parameters of the sound response has not been tested” in light of the Li 2021 study.Decoding analysis:The classifier accuracy is not significantly different between the offset and onset windows, which suggests that onset responses can be as predictive of the mouse’s offset detection behavior as offset responses. Could the authors please discuss?Reviewer #3:The paper represents an innovative and comprehensive body of work aimed to assess the function of cortical offset responses in the anterior auditory field in sound perception. Whereas the majority of work to date in auditory neuroscience has focused on the sound onset responses , the offset responses, which are present in the cortex, and especially, as the authors show, in AAF, have received less attention. The offset responses can play an important role in sound segregation and auditory scene analysis. The authors used a combination of behavioral, electrophysiological and optogenetic techniques to study the properties of cortical offset responses. The authors first test whether and how offset responses correlate and affect behavioral detection of sound offsets. They find that suppressing offset responses in AAF reduces the responses of mice to sound offsets and that there is a significant correlation between cortical responses and behavioral report. The authors next use elegant electrophysiological and manipulation methods to find that cortical offset responses have a component that is generated in the AAF and therefore not only inherited from the periphery. The authors also find that offset responses increase with sounds that have longer duration and therefore do not simply encode for silence. Behaviorally, authors provide evidence for a role of cortical offset responses in sound termination perception. Such an extensive description of these types of responses provides for a substantial advance in our understanding of cortical function.The authors have conducted additional experiments and analysis and the revised manuscript is much improved. We have only 1 outstanding concern.Responses to 1+2: We believe that it is important to consider not only the hit rate, but also other signal detection measures in interpreting mouse behavior. In fact, trials on which the mouse licked during sound may be informative, and I am not sure it’s warranted to not include information about them in the analysis. One possible approach would be to compute signal detection theory measures of HR (hit rate), CR (correct rejects), M (misses rate) and FA , from which one can compute d’ using standard approaches.– Consider only the trials in which the sound is presented for either 1 s or 2 s.– To compute hits, compute the percent of 1s-long trials, in which the mouse licked between 1s and 2 s (during the reward window).– To compute misses, compute the percent of 1s-long trials, in which the mouse did not lick between 1s and 2 s (during the reward window).– To compute FA, compute the percent of 2s-long trials, in which the mouse licked between 1s and 2s (during the last 1s of sound).– To compute CR, compute the percent of 2s-long trials, in which the mouse did not lick between 1s and 2s (during the last 1s of sound).Response to 10. Please use <= 2 significant digits for p values (e.g. p = 0.5655 should be reported as p=0.57 on line 415). [Editors’ note: the authors resubmitted a revised version of the paper for consideration. What follows is the authors’ response to the first round of review.]Reviewer #1:There has been a great deal of recent interest in the neural basis for offset responses given their hypothesised importance to perception. The possible behavioural relevance to cues like sound duration and gap duration has been taken as a self-evident truth in some work. I found this work attractive in actually testing the relevance of offset responses to duration perception in a mouse model in addition to examining the brain basis. The work is thorough and well executed. The work demonstrates offset responses that occurs for the first time in auditory cortex distinct from A1 where prevention of offsets by activating cells causes worsening of behavioural performance.1. I think an initial concern in discussion of this manuscript about artefactual effects of spectral splatter due to abrupt sound termination have been addressed in this version of the manuscript.We are pleased to read that the description of the possible effects of spectral splatter, as described in the submitted manuscript, removed this concern.2. The data appear to support a specialisation for offset response in AAF but have offset responses and their behavioural relevance been examined in A1? The analysis in figure 4 convincingly demonstrates changes in offset responses between MGB and AAF (which is monosynaptic connection) but it would be interesting to know about A1. I appreciate there is a strong prior related to AAF based on previous work but the offset responses being in AAF has been almost taken as a given in manuscript.We thank the reviewer for this comment. We analyzed offset responses from awake recordings in both A1 and AAF for sounds with a duration of 500 ms . As offset responses at the population level are minimal in A1 compared to AAF, we think that their contribution in a sound detection task will be much more difficult to evaluate. We, therefore, decided to focus on AAF in this manuscript.In the revised manuscript, this information has now been added (l.91-95), as Figure S1.(l.91-95) “As, at the cortical level, transient offset responses cluster within AAF rather than A1  and are absent in the averaged population activity of A1 neurons in response to 9 kHz pure tones in awake preparations , we decided to focus on AAF to assess the behavioral relevance of cortical offset responses.”Reviewer #2:In this study, the authors examine: (1) whether offset responses, where neurons respond upon termination of a stimulus, are behaviorally relevant; (2) whether offset responses are merely inherited from subcortical stations or are generated and amplified in cortex; and (3) whether offset responses simply encode sound termination or if they carry stimulus identity information as well. They show, using a combination of optogenetics and behavior, that suppressing offset responses in auditory field AAF results in an impairment of sound termination detection. They then show, using single- and multi-unit recordings, that the behavioral choice of the animal can be decoded on a trial-by-trial basis from the offset and late response phases. Finally, using antidromic stimulation and using multiple stimuli, the authors show that AAF offset responses are not wholly inherited from the auditory thalamus.The electrophysiological elements of the study seem solid and well-performed. Some weaknesses of the study include the effectiveness of task acquisition by the behavioral subjects, and behavioral analyses that discard trials with potentially useful information. Some statistical tests may not be appropriate and brings into question the results of the decoding analysis. Very recent and highly relevant publications are not discussed in the study. Additional control analyses would strengthen the manuscript.1. Two very recent studies address questions that are central to this manuscript. First, Li H et al.  show using optogenetic manipulations in primary auditory cortex (A1) that A1 OFF activity is required for the perception of sound duration. These results must be discussed in the context of the authors claim that AAF might be specialized for the detection of offset responses.These two papers were not published yet when we initially submitted our manuscript. We fully agree that they are relevant to this manuscript and should be cited and discussed.In the revised manuscript, the Li et al. paper is discussed on l.565-570 and 610-613:(l.565-570) “It was recently shown that A1 transient offset responses to noise stimulation underlie the perception of sound duration . As AAF supplies <inline-graphic mime-subtype="png" mimetype="image" xlink:href="media/image1.png" />45% of the cortical input to A1  and the modulation of acoustic information between A1 and AAF in the cat’s Acx was shown to be dominated by a unidirectional AAF to A1 pathway , it would be interesting to understand if A1 offset responses originate from AAF.”(l. 610-613) “Additionally, the lack of increase in offset response amplitude with the duration of WN bursts  suggests that a stimulus has to contain a spectral structure to evoke duration dependent offset responses .”2. Second, Bondanelli et al.  argue for a role of recurrent A1 connectivity in shaping offset responses in cortex, including the fact that the offset response carries information about stimulus type. These results should be discussed in the context of the authors observations as well.In the revised manuscript, the Bondanelli et al. paper is discussed on l. 570-573:(l. 570-573) “Very recently, Bondanelli et al. argued for a role of recurrent A1 connectivity in shaping offset responses in cortex, including the fact that the offset response carries information about stimulus type . The high level of information exchange between A1 and AAF raises the question of whether these mechanisms in A1 depend on AAF activity.”3. Regarding behavior: the authors discard trials from analyses when the animal licked while the tone was ongoing, and this appears problematic. From the description in the methods, it is unknown what fraction of total trials were discarded from analyses. These trials could be coded as false alarms, and when this information is included in the analysis by using a metric such as the sensitivity index (d’), could provide a complete picture of the behavior.Considering 30% correct trials as ‘trained’ seems well below traditionally accepted metrics of when a animal is considered trained, especially for a relatively simple detection task. Usually, this number is closer to 70% correct – for example, in the Li et al. 2021 paper mentioned above, mice were considered trained after reaching 90% correct trials on a sound duration discrimination task. Better yet, a d-prime of 1 or 1.5 when false alarms are also considered is a more sensitive metric of behavior .We thank the reviewer for this comment. We fully agree that the analysis of the behavioral experiments would benefit from the calculation of a false alarm rate and that the original manuscript was lacking a detailed description of the behavioral paradigm. However, false alarm rates can be calculated for discrimination tasks  but not for detection tasks. Likewise, unlike in a discrimination task, a hit rate of 50% is not a chance level in a detection task. To assess the rate at which mice would lick in case they would not detect sound termination, we calculated the lick rate in a window following the reward window and defined this as the chance level or random lick rate. Comparing this random lick rate with the hit rate (licks during the reward window) allows us to demonstrate that mice were performing the task and could detect sound termination, well above chance . This confirms the high relevance of our behavioral data.To quantify the behavioral influence of offset responses, especially when manipulating them such as what is done with optogenetics in Figure 1 or with offset ramp duration in Figure 2, the mice had to be able to hear sound termination and to be ready to react. If they licked before sound termination, that would not be possible. We, therefore, removed all trials where the mice licked before sound termination. Compared to a normal detection task (detection at the onset of a sound), that would be analogous to remove the trials where the animals would lick before the stimulus, as is commonly done in the field.In the revised manuscript, we have:– Added Figure S2 and updated Figure 1.– Added the corresponding information on .103-107.(l.103-107) “The chance level of licks was calculated in a late window following the end of the reward window . An increase in correct offset detection (hit rate), a decrease in reaction time over the training sessions , and a significant difference between hit rate and licks at the chance level  reflected successful learning after which animals were moved to the test phase .”4. In decoding of performance from activity, given that the reward window opens at offset and is open for only 1 s, the inclusion of the ‘late’ phase is problematic unless it can be shown that licks do not occur within 0.5 s of sound offset. This bump for the hits could result from multiple effects – movement, reward, licking sounds etc. The data supporting the claim of better decoding from offset responses hinges on Figure 3c, where offset responses yield greater accuracy than onset responses. However, pairwise Wilcoxon tests do not seem appropriate for these data where multiple comparisons are being made. The authors should use an ANOVA or Kruskal-Wallis test followed by by multiple-comparisons corrected posthoc tests.We agree that the ‘late’ bump in the hit trials is probably due to movement or reward, and discuss this on l. 560-562:(l.560-562) “The neural activity during hit trials in AAF could be influenced by the animal’s general motivation , motor-related inputs , or reward expectations .”The decoding performed on the ‘late’ phase serves as a control, confirming that specific patterns of activity within individual neurons reflect an action of the animal, hence leading to better decoding.Regarding statistics, we thank the reviewer for noticing the inappropriateness of the test previously used. We have now used the Friedman test (nonparametric ANOVA) with multiple comparisons. Figure 3 has been updated accordingly in the revised manuscript.5. From Figure 1E, it appears that the post-inhibitory rebound in other cells in the laser on condition has a similar magnitude to the offset response in the laser off condition. Could the rebound be driving AAF responses that signal an offset, albeit delayed by about 0.2 s, that the animals could be using to detect sound termination? To answer this question, could the authors analyze both the neurophysiological data, as well as determine if the correct responses in the laser ON condition have longer latencies consistent with this 0.2 s delay?We have now compared the reaction times for trials with and without laser. We did not see any difference in reaction times for trials with and without laser when sounds were terminated with a fast ramp. Interestingly sounds terminated with a slower ramp with laser stimulation were detected significantly slower than when the laser was not applied .This delay in reaction time due to the laser is 15 ms on average. If this delay were due to the reaction to the rebound created by the laser, we would expect the delay to be of the duration of the laser application, i.e., 200 ms. The rebound can, therefore, not justify this reaction time difference. We think that the delay could instead be explained by the fact that with the laser on, offset responses are decreased and the sound termination detection therefore more difficult, leading to a delayed behavioral response.In the revised manuscript, Figure 1—figure supplement 4 has been added as Figure S3. The corresponding description is on l.121 and l.214:(l.121-122) “Interestingly, reaction times were significantly longer in laser on compared to laser off trials .”(l.214-215) “There was no significant difference in the reaction times for both tested ramps .”6. If the authors have the data available, it would be great to see a similar control as shown in Figure 2j-m for the longer ramp duration as well in Figure 1. More detail in the methods section as to how the fiber was placed over AAF (in craniotomy but above dura?), whether it was optically shielded to prevent visual cues etc. would be helpful.We have now added the light-control experiment results also for the long ramp (10 ms). In the revised manuscript, they are now integrated within Figure 1.All our behavioral experiments with optogenetic manipulations were coupled with electrophysiological recording. For all behavioral sessions, we first identified AAF based on the tonotopic gradient obtained with electrophysiological recordings. We then placed an optical fiber above AAF (above the dura) and titrated the laser (473 nm) power such that it would remove offset responses. Finally, we performed the behavioral session with these specific optogenetic parameters (optic fiber placement and laser power) and confirmed at the end of each session that offset responses were removed during the laseron trials. No optical shield was used. Control experiments confirmed that the laser did not give any visual cue .In the revised manuscript, we have added this information in the Results section:(l.109-116) “All our behavioral experiments with optogenetic manipulations were coupled with electrophysiological recording. For all behavioral sessions, we first identified AAF based on the tonotopic gradient obtained with electrophysiological recordings. We then placed an optical fiber above AAF (above the dura) and titrated the laser (473 nm) power to remove offset responses. Finally, we performed the behavioral session with these specific optogenetic parameters (optic fiber placement and laser power). We confirmed at the end of each session that offset responses were removed during the laser-on trials. No optical shield was used.”7. For the onset-offset neurons that do not have a sustained response profile, it is clear that the highly correlated offset is an important distinguishing cue – it provides a high-SNR signal between the offset response and the previous silent period (when the tone is on). But what if  some amount of sustained activity is present? Is offset-detection behavior worse, and is decoding accuracy using the classifier also worse? If behavioral data is not available, could additional analyses be performed to predict sound termination time for pure tones and white noise, and make a prediction as to what would happen behaviorally?As offset responses are much smaller for white noise than for pure tones, we believe that their contribution in the sound detection task will be much smaller than the larger offset responses for pure tones. Additionally, offset responses evoked by WN bursts are not increasing as a function of sound duration . On the other hand, there is a strong ongoing activity in AAF when longer sounds are presented , raising the question of offset response relevance for this type of sound. We agree that evaluating the role of this sustained activity in the sound detection task is a most interesting point, but we feel that it goes beyond the scope of this manuscript, which is to evaluate the role of offset responses in particular. To understand to which extent onset, sustained, and offset activity contribute to sound termination perception and whether the contribution of all response types differs as a function of the spectral complexity of the stimuli requires significantly more behavioral studies. We are however following up on the role of sustained activity and its relation to onset and offset responses, but plan to address this in another study.We refer to this figure in the results and Discussion sections.(l.471-473) “This extended firing could affect the generation of offset for WN stimulation. Offset responses evoked by WN were not increasing as a function of sound duration in neither MGB nor AAF .”(l.610-613) "Additionally, the lack of increase in offset response amplitude with the duration of WN bursts  suggests that a stimulus has to contain a spectral structure to evoke duration dependent offset responses . "Reviewer #3:The goal of this study was to assess the function of cortical offset responses of the anterior auditory field (AAF) in sound perception. The authors used a combination of behavioral, electrophysiological and optogenetic techniques to study the properties of cortical offset responses. Through behavioral experiments combined with optogenetics, the authors first claim to find that inhibiting offset responses in the AAF decrease the mouse's ability to detect when a sound ends. Furthermore, they report that larger offset responses correlate with an increase in the mouse's ability to detect sound termination. Functionally, the authors demonstrate via electrophysiological experiments that cortical offset responses have a component that is generated in the AAF and therefore not only inherited from the periphery. The authors also find that offset responses increase with sounds that have longer duration and therefore do not simply encode for silence. The electrophysiological investigation of the properties of cortical offset responses is well designed and the conclusions are justified by the data. However, several questions about the behavioral paradigm arose that warrant further control experiments and re-examining interpretation.1) The behavioral paradigm suffers from a design in which it is difficult to estimate the false alarm rate. Therefore, it is unclear whether the mouse is trained to lick in response to tone offsets, or rather to reduce licking during the sound presentation. The criterion for "fully trained" is set at 30% hit rate, well below chance , which seems somewhat low. It is unclear what the mouse licking is reporting.We thank the reviewer for this comment. We fully agree that the analysis of the behavioral experiments would benefit from the calculation of a false alarm rate and that the original manuscript was lacking a detailed description of the behavioral paradigm. However, false alarm rates can be calculated for discrimination tasks but not for detection tasks. Likewise, unlike in a discrimination task, a hit rate of 50% is not a chance level in a detection task. To assess the rate at which mice would lick in case they would not be able to detect sound termination, we calculated the lick rate in a window following the reward window and defined this as the chance level or random lick rate. Comparing this random lick rate with the hit rate (licks during the reward window) allows us to clearly demonstrate that mice were really performing the task and were able to detect sound termination, well above chance . This confirms the high relevance of our behavioral data.In the revised manuscript, we have:– Added Figure S2 and updated Figure 1.– Added the corresponding information on l.103-107.(l.103-107) "The chance level of licks was calculated in a late window following the end of the reward window . An increase in correct offset detection (hit rate), a decrease in reaction time over the training sessions , and a significant difference between hit rate and licks at the chance level  reflected successful learning after which animals were moved to the test phase ."2) This interpretation of behavioral performance is complicated by the high rate at which mice licked during the trials . (Is the legend for figure S1 correct?) Since the authors report that "Trials with licks at the tone onset were discarded from the analysis ", question arises whether 60% of the trials were excluded for some mice. In that case, is the lick rate on the trials that were kept simply by chance?We explain in the previous point how we now calculate the chance level.To quantify the behavioral influence of offset responses, especially when manipulating them such as what is done with optogenetics in Figure 1 or with offset ramp duration in Figure 2, the mice had to be able to hear sound termination and to be ready to react. If they licked before sound termination, that would not be possible. We, therefore, discarded all trials where the mice licked before sound termination. Compared to a normal detection task (detection at the onset of a sound), that would be analogous to remove the trials where the animals would lick before the stimulus, as is commonly done in the field.3) The optogenetic manipulation of AAF during behavior drives a relatively small (20%) reduction in performance . It is therefore unclear that the conclusion that AAF is necessary for sound offset detection is supported by the data. The behavioral + optogenetic paradigm should be better described in methods, allowing to better assess the presentation of the laser (see point 8).We agree with the reviewer that it is not possible to conclude that AAF is necessary for sound offset detection. What we claim is that AAF offset responses help sound termination detection and that they have a behavioral role, as clearly stated on l.15, l.120, l.258, or l.541 of the manuscript – not that they are necessary. As discussed, information from non-lemniscal areas (l.629-631) or sustained activity (l.604-606) could, for example, also play a role.The relatively small reduction in performance (20 %) should be interpreted knowing that the laser light was applied unilaterally – this number might increase if the manipulation is bilateral. Despite this, our data clearly demonstrate that transient offset responses in AAF improve the detection of sound termination.All our behavioral experiments with optogenetic manipulations were coupled with electrophysiological recording. For all behavioral sessions, we first identified AAF based on the tonotopic gradient obtained with electrophysiological recordings. We then placed an optical fiber above AAF (above the dura) and titrated the laser (473 nm) power such that it would remove offset responses. Finally, we performed the behavioral session with these specific optogenetic parameters (optic fiber placement and laser power) and confirmed at the end of each session that offset responses were removed during the laseron trials. No optical shield was used. Control experiments confirmed that the laser did not give any visual cue .In the revised manuscript, we have added this information in the Results section:(l.109-116) "All our behavioral experiments with optogenetic manipulations were coupled with electrophysiological recording. For all behavioral sessions, we first identified AAF based on the tonotopic gradient obtained with electrophysiological recordings. We then placed an optical fiber above AAF (above the dura) and titrated the laser (473 nm) power to remove offset responses. Finally, we performed the behavioral session with these specific optogenetic parameters (optic fiber placement and laser power). We confirmed at the end of each session that offset responses were removed during the laser-on trials. No optical shield was used."4) The reduction in performance may be due to general laser presentation and not specific to sound offset. It would be useful to present the laser during the sound or at different points in the stimulus to better understand how its presentation relates to offset responses specifically .First, we hope that the more detailed explanation of our behavioral experiment  will clarify how the optogenetic manipulations were performed and how the results relate to offset responses specifically. Then, we have performed control experiments to show that the laser itself did not cause any change in behavioral performance and that it is really its effect on offset responses that explains the change in behavioral performance . Finally, we have performed additional experiments in PV-ChR2 animals where the laser light was applied for 200 ms during the sound presentation . The laser light (473 nm) was applied for 200 ms starting at 200 ms following sound onset. The laser light activated PV+ cells in AAF, which in turn suppressed the activity of other AAF cells . This laser manipulation did not result in any specific change in the animals’ performance , strengthening our results with the laser application at the sound termination.These data are now incorporated within Figures 1 and 2 and described in the result section l.126-132 and l.262-268.(l.126-132) "Finally, we performed experiments in PV-ChR2 animals where the laser light was applied for 200 ms during the sound presentation and evaluated the animals' performance in the sound termination detection task . The laser light was applied for 200 ms starting at 200 ms after sound onset. The laser light activated PV+ cells in AAF, which in turn suppressed the activity of other AAF cells . The laser presentation during the ongoing sound did not result in any significant change in the animals’ performance  = 2.6±2.7%)."(l.262-268) "Finally, we performed experiments in PV-ChR2 animals where the laser light was applied for 200 ms during the sound presentation . The laser light activated PV+ cells in AAF, which in turn suppressed the activity of other AAF cells . The laser presentation during the ongoing sound did not result in any significant change in the animals’ performance  = 2.7±3.1%). Together, these experiments confirm that changing offset responses, but not sustained activity, in AAF influences behavioral performance."5) It would be helpful to present more details about how the classifier was trained and tested . The neuronal classifier also does not seem to predict accurately behavior as the accuracy for the offset responses is at ~65 % . It is furthermore interesting that much of behavior can be explained based on spontaneous neuronal activity, and therefore an alternative interpretation would be that cortical state . This also suggests that a control as suggested in point 4 (laser presented at different time points in the stimulus) would be useful, as the classifier would predict significant reduction in performance based on suppression of activity prior to sound, and not at sound offset.Details on how classifier was trained are now added to the methods section.(l.827-838) "Logistic regression was implemented using the sklearn function LogisticRegression with the lbfgs solver and L2 regularization to avoid over-fitting. 8-fold cross validation was performed by leaving out a random 12,5% subset of trials to test the classifier performance, and remaining trials were used to train the classifier. A range of regularization values was tested (0.0001 to 10000 log spaced), and the one that gave the smallest error on the validation dataset was chosen as the optimal regularization parameter. The classifier accuracy was computed as the percentage of testing trials in which the animal's choice was accurately predicted by the classifier and summarized as the average across the 10 repetitions of trial subsampling. The spiking activity of each neuron was z-scored before running the logistic regression model. Trial labels were shuffled to confirm that decoding is not working for random data. This procedure was repeated 10 times. Then the average across the 10 repetitions was used to assess classifier accuracy for randomized data. "As suggested, we also trained a classifier based on the offset responses preceded by activity suppression prior to the sound termination . We founEssential revisions:Reviewer #2:I appreciate the authors' detailed responses to the concerns arising from the earlier review of this manuscript. There are no major concerns with the electrophysiology part of the revised manuscript.Behavior: My concerns with behavioral experiments and what the mice are actually reporting in the task remain.1) I respectfully disagree with the authors' statement that false alarm rates cannot be calculated for detection tasks. The term 'False Alarm' is derived from signal detection theory, where the subject reports that a signal was present when a signal was not actually present. In this case, the signal is the offset. If a mouse reports an offset by licking during the sound , that outcome should be a false alarm. In the authors' task design, only correct rejections cannot be specified . I agree with the authors that if the mouse starts licking before even the sound onset, those trials should be discarded.We thank the reviewer for suggesting to use not only hit rate, but also other signal detection measures in quantifying mouse behavior. Following the advice of Reviewer #3, we have now calculated hit, correct rejection, miss, and false alarm rates based on trials in which the sound is presented for either 1 s or 2 s. The behavioral performance is then calculated as the ratio of desirable and undesirable outcomes, as you suggest in your next comment. We call this measure the offset detection index, or ODI:ODI=Hit+CorrectRejectionMiss+FalseAlarm Hit: percent of 1 s-long trials in which the mouse licked between 1 s and 2 s (during the reward window).Miss: percent of 1 s-long trials in which the mouse did not lick between 1 s and 2 s (during the reward window).False Alarm: percent of 2 s-long trials in which the mouse licked between 1 s and 2 s (during the last 1 s of sound).Correct Rejection: percent of 2 s-long trials in which the mouse did not lick between 1 s and 2 s (during the last 1 s of sound).2) "Likewise, unlike in a discrimination task, a hit rate of 50% is not a chance level in a detection task."- It is not a question of what the chance level was, but rather a question of whether the mouse was reliably detecting the offset. p(Hit) + p(FA) + p(Miss) + p(CR) = 1, and a measure of performance would take into account the ratio of desirable (p(Hit)+p(CR)) and undesirable (p(FA)+p(Miss)) outcomes. To illustrate, in Figure S2 panel h, I count 50 trials, with n(Hit) = 23, n(Miss) = 11, nFA = 16, and n(CR) = 0. In this counting, the number of desirable outcomes (23+0=23) is lesser than the number of undesirable outcomes (11+16 = 27), and would result in a d' = z(Hits) – z(FA) = 0.37. However, if trials in which animals licked during the sound (which I coded as FA) are dropped, then n = 50-16 = 34, with n(Hits) = 23 (23/34 = 67%). This is why dropping the onset lick trials and considering 30% hits as reaching criterion for training is not convincing.Other metrics to better capture the behavior might be a discrimination index as used in, for example, Schwartz and David, 2018. Here the cumulative lick probability can be used to calculate ROC curves.As described above, an offset detection index (ODI) is now used to compare the animals’ performance for different conditions . Taking into account hits, misses, correct rejections, and false alarms confirms that reducing cortical offset responses with optogenetics significantly decreases the behavioral performance in sound termination detection task . This is in accordance with our previous conclusions made based on the analysis of the hit rates alone and strengthen our results.In the revised manuscript, we have:– Updated Figure 1 and Figure 2 with ODI instead of hit rates.– Added the corresponding ODI values to the Results section .– Added the detailed description on how ODI was calculated to the methods section (l. 823-836).– Moved the behavioral measures of hit rate previously shown in Figures 1 and 2 to Figure 1 —figure supplement 3 of the revised manuscript.3) Page 5, lines 121 and 125: The authors use a very similar effect size change in hit rate for PV-Cre animals (4.5 +/- 2%) and for wild type animals (4.4 +/- 2.4%) to make the claim that there was a significant change in hit rate (p = 0.0264) for PV-cre animals, but no change (p=0.058) for the wild type animals . The appropriate comparison in this case would be between 1H and 1L. . It would be instructive if the authors could plot the Laser OFF and ON hit rates separately for the two groups  so that one can also appreciate if there are overall differences in performance levels.As described in the previous point, we are now using the offset detection index (ODI) to evaluate the animals’ behavior performance in the sound termination detection task. In the updated version of the manuscript, we report the effect size change of ODI for PV-ChR2 and WT animals . We also compared the general performance of the animals but did not see any significant differences in the overall performance levels ?We thank the Reviewer for this comment. Indeed, panel H represented SD and panel L SEM, while in the main text we reported SEM values. In the updated version of the manuscript, the difference in ODI are represented as mean± SEM in Figures 1 and 2 and the corresponding values are reported in the main text.Citing recent literature:I appreciate the authors' inclusion of two recent studies in the Discussion section. However, these studies also affect the framing of the manuscript in the introduction section.Page 2, line 39: "De-novo generation or amplification of offset responses in these areas have not been demonstrated yet." – Please rephrase and appropriately cite the Bondanelli 2021 study here.We have now updated the introduction of the manuscript with this citation.(l.37-44) “Offset responses in MGB and ACx are generally thought to be driven by excitatory/inhibitory inputs from IC rather than by other neural mechanisms . Recently, Bondanelli et al. also argued for a role of recurrent A1 connectivity in shaping offset responses in cortex, and suggested that cortical offset responses could be generated at a higher level of recurrency . However, de novo generation or amplification of offset responses in MGB and ACx have not been demonstrated experimentally yet .”Page 2, lines 42 – 52: In this paragraph discussing the perceptual significance of offset responses, please appropriately discuss the Li 2021 study. Please rephrase the last sentence of this paragraph.We have now updated the introduction of the manuscript with this citation.(l.53-56) “Recently, it was shown that A1 transient offset responses contribute critically to encoding and perceiving sound duration . Whether the increased neuronal activity of sound offset responses accounts for other perceptual skills is unclear.”Page 4, line 86: Please rephrase "Changing the neuronal activity of sound offset responses without changing any other parameters of the sound response has not been tested" in light of the Li 2021 study.It is true that sound offset responses have also been manipulated in the Li et al. 2021 study. The test used was however a duration discrimination task, and not a sound termination detection task as in our study. We have now updated the introduction of the manuscript accordingly.(l.88-l.91) “The perceptual significance of cortical offset responses has been difficult to assess. Indeed, confounds about perceiving a sound and its termination are intricately linked. Changing the neuronal activity of sound offset responses to evaluate its contribution in sound termination detection has not been tested.”Decoding analysis:The classifier accuracy is not significantly different between the offset and onset windows, which suggests that onset responses can be as predictive of the mouse's offset detection behavior as offset responses. Could the authors please discuss?This is now discussed in the Results section.(l.292-295) “These results suggest that AAF offset responses can be informative on the animal's decision emphasizing the behavioral relevance of AAF offset responses. However, the classifier accuracy was not significantly different between the offset and onset windows, suggesting that perceiving a sound and its termination are intricately linked.”Reviewer #3:The paper represents an innovative and comprehensive body of work aimed to assess the function of cortical offset responses in the anterior auditory field in sound perception. Whereas the majority of work to date in auditory neuroscience has focused on the sound onset responses , the offset responses, which are present in the cortex, and especially, as the authors show, in AAF, have received less attention. The offset responses can play an important role in sound segregation and auditory scene analysis. The authors used a combination of behavioral, electrophysiological and optogenetic techniques to study the properties of cortical offset responses. The authors first test whether and how offset responses correlate and affect behavioral detection of sound offsets. They find that suppressing offset responses in AAF reduces the responses of mice to sound offsets and that there is a significant correlation between cortical responses and behavioral report. The authors next use elegant electrophysiological and manipulation methods to find that cortical offset responses have a component that is generated in the AAF and therefore not only inherited from the periphery. The authors also find that offset responses increase with sounds that have longer duration and therefore do not simply encode for silence. Behaviorally, authors provide evidence for a role of cortical offset responses in sound termination perception. Such an extensive description of these types of responses provides for a substantial advance in our understanding of cortical function.The authors have conducted additional experiments and analysis and the revised manuscript is much improved. We have only 1 outstanding concern.Responses to 1+2: We believe that it is important to consider not only the hit rate, but also other signal detection measures in interpreting mouse behavior. In fact, trials on which the mouse licked during sound may be informative, and I am not sure it's warranted to not include information about them in the analysis. One possible approach would be to compute signal detection theory measures of HR (hit rate), CR (correct rejects), M (misses rate) and FA , from which one can compute d' using standard approaches.– Consider only the trials in which the sound is presented for either 1 s or 2 s.– To compute hits, compute the percent of 1s-long trials, in which the mouse licked between 1s and 2 s (during the reward window).– To compute misses, compute the percent of 1s-long trials, in which the mouse did not lick between 1s and 2 s (during the reward window).– To compute FA, compute the percent of 2s-long trials, in which the mouse licked between 1s and 2s (during the last 1s of sound).– To compute CR, compute the percent of 2s-long trials, in which the mouse did not lick between 1s and 2s (during the last 1s of sound).We thank the reviewer for suggesting to use not only hit rate, but also other signal detection measures to quantify behavioral performance. As suggested, we have now calculated hit, correct rejection, miss, and false alarm based on trials in which the sound is presented for either 1 s or 2 s. The behavioral performance is then calculated as the ratio of desirable and undesirable outcomes, as suggested by Reviewer #2. We call this measure the offset detection index, or ODI:ODI=Hit+Correct RejectionMiss+FalseAlarmODI is now used to compare behavioral performance for different conditions . Taking into account hits, misses, correct rejections, and false alarms confirms that reducing cortical offset responses with optogenetics significantly decreases the behavioral performance in the sound termination detection task . This is in accordance with our previous conclusions made based on the analysis of the hit rates alone and strengthens our results.In the revised manuscript, we have:– Updated Figure 1 and Figure 2 with ODI instead of hit rates.– Added the corresponding ODI values to the Results section .– Added the detailed description on how ODI was calculated to the methods section (l. 823-836).– Moved the behavioral measures of hit rate previously shown in Figures 1 and 2 to Figure 1 —figure supplement 3 of the revised manuscript.Response to 10. Please use <= 2 significant digits for p values (e.g. p = 0.5655 should be reported as p=0.57 on line 415).We have now updated the reported p values to 2 significant digits.


Conventional cytological diagnosis including duct-washing cytology (DWC) is sometimes performed using ductal epithelial cells collected during mammary ductoscopy; it is useful for detection of early-stage breast cancer such as ductal carcinoma in situ (DCIS). However, conventional cytological diagnosis focuses exclusively on cellular morphology; false negatives and false positives may be caused by inadequate specimen preparation (triggering cell degeneration) or poor examiner diagnostic skills. Molecular diagnosis using RNA biomarkers is expected to compensate for the weaknesses of cytological diagnosis. We previously employed microarray analysis to identify highly expressed genes in DCIS, suggesting that they may be useful for DCIS diagnosis. Here, we explored whether DWC samples yielded RNA of sufficient quantity and quality for RNA biomarker-based diagnosis.We extracted RNAs from 37 DWC samples. RNA from 12 samples exhibited RNA integrities of  ≥ 6, indicative of moderate-to-high quality. We then showed that cocaine and amphetamine regulated transcript prepropeptide (CARTPT) and breast cancer-associated transcript 54 (BRCAT54) mRNA—previously shown by microarray analysis to be highly expressed in DCIS—were detectable in these samples. Therefore, DWC samples may be useful for molecular diagnosis involving RNA biomarkers.The online version contains supplementary material available at 10.1186/s13104-022-05928-1. Breast cancer (BC) originates in the mammary ducts. Some cancers may be metastatic, thus progressing from non-invasive to invasive. Because of substantial advances in breast imaging tools, ductal carcinoma in situ (DCIS) constitutes approximately 20% of newly diagnosed BCs; its proportion is increasing , 2. EarlMammary ductoscopy (MD) allows direct visualisation of the ductal lumen using a fibreoptic microendoscope, as well as the collection of cytological specimens for duct-washing cytology (DWC); this is an alternative form of preoperative BC diagnosis. It would be clinically useful if surplus DWC specimens could be used for RNA extraction and subsequent molecular diagnosis involving RNA biomarkers. Here, we explored whether RNA of sufficient quantity and quality could be recovered from DWC specimens of DCIS patients. Furthermore, we explored whether the RNA contained cocaine- and amphetamine-regulated transcript prepropeptide (CARTPT) and breast cancer-associated transcript 54 (BRCAT54) mRNAs, which a previous microarray study found to be highly expressed in DCIS .The surplus tissues of 45 specimens collected for DWC at Musashi Kosugi Hospital from December 2017 to October 2019 were used. All patients presented with ND. Observation and specimen sampling were carried out as follows. First, nipple duct dilatation was performed using bougies; an 18-G Surflo intravenous catheter was then inserted. The duct was washed with a lidocaine solution, and the solution was collected. Second, Solid Fibrescope MS-611  was inserted and the lesion was directly observed. MD allows direct observation of the ductal interior; a fibre of outer diameter 0.8 mm is placed within an 18-G Surflaw needle. Finally, aspiration biopsy was performed using an 18-G Surflo intravenous catheter after removal of the fibrescope [Total RNA was extracted from specimens using RNAiso-Plus , generally in accordance with the manufacturer’s instructions; however, all steps were performed below 4 °C and linear acrylamide  was added as a co-precipitant at the step of isopropyl alcohol precipitation . The A260/A280 ratios of RNAs extracted from DWC specimens were determined using a NanoDrop 1000 spectrophotometer (Thermo Fisher Scientific).The extracted RNA was further analyzed using the Agilent 2100 Bioanalyzer  in combination with the RNA 6000 Nano Lab Chip kit (Agilent). The quality of RNA was assessed by RNA integrity number (RIN) .The levels of mRNAs encoding CARTPT, BRCAT54, and hypoxanthine phosphoribosyltransferase 1 (HPRT-1)  were measured via quantitative reverse-transcription (qRT)-PCR using the SYBR Green Assay (Thermo Fisher). PCR products were monitored on a DICE Real-Time PCR System (Takara-Bio).The primer sequences  were: CARTPT forward, AGAAGGAGCTGACGAAGCG; CARTPT reverse, ACACAGCTTCCCGATCCTTG; BRCAT54 forward, CCTGCAGGAAAATGCAGTGAAG; BRCAT54 reverse CACCATCACATTGCTGACTTCCA; and HPRT1 forward, CATTATGCTGAGGATTTGGAAAGG; HPRT1 reverse CTTGAGCACACAGAGGGCTACA. Our qRT-PCR analyses to evaluate CARTPT, BRCAT54, and HPRT-1 mRNA levels were performed in triplicate.Of the 37 patients who underwent MD, eight underwent surgery and seven turned out to have malignant tumors. For immunopathological diagnosis, they underwent immunobiological staining for estrogen receptor (ER), progesterone receptor (PR), and human epidermal growth factor receptor 2 (HER2)  with additional biostatistical functions .Figure Patient information including ages, DWC diagnoses, MD findings, and the RINs and amounts of isolated RNAs are shown in Table RNAs of moderate-to-high quality (RIN  ≥ 6) were isolated from 12 samples  ; four were benign (four IDP). During the follow-up period, the tumours of two patients became malignant. Patient no. 16 was recently diagnosed with BC by puncture of a peripheral mass. Patient no. 27 eventually underwent surgery and diagnosed as mucinous carcinoma located in the peripheral region of the duct. We selected five samples  with high RNA yields (Table To the best of our knowledge, no report about RNA extraction from MD samples has been published so far, although there are a few reports describing the analysis of DNA methylation in MD samples , 15. ThiIn summary, we used MD to examine mass lesions in the mammary ducts and extracted RNA from DWC samples; many extracts exhibited moderate-to-high RNA quality. We found that mRNAs abundantly expressed in SPC or IDP can be detected via qRT-PCR.There are some limitations in this study. For example, the sample size of this study is small, which conceivably weakens the results deduced from the provided data. On a relevant note, the clinical usability of BRCAT54 and CARTPT mRNAs as biomarkers has not been evaluated. Further, our method is currently applicable only for patients who exhibit mass lesions in their mammary ducts and may be difficult to apply for patients with small intraductal lesions, because only a few cells can be conceivably recovered from such small lesions.Additional file 1: Figure S1. Flowchart of RNA extraction. Figure S2. Detection of BRCAT54-, CARTPT- and HPRT-1-encoding mRNAs in RNA isolated from representative DWC samples. Table S1. Pathological findings.