To study such dynamic algorithms from mass neural signals, we recorded the magnetoencephalographic (MEG) activity of participants who resolved the classic XOR, OR, and AND functions as overt behavioral tasks . Each function requires a different computation over the same inputs to produce the task-specific behavioral outputs. In each task, we found that source-localized MEG activity progresses through four computational stages identified within individual participants: (1) initial contralateral representation of each visual input in occipital cortex, (2) a joint linearly combined representation of both inputs in midline occipital cortex and right fusiform gyrus, followed by (3) nonlinear task-dependent input integration in temporal-parietal cortex, and finally (4) behavioral response representation in postcentral gyrus. We demonstrate the specific dynamics of each computation at the level of individual sources. The spatiotemporal patterns of the first two computations are similar across the three tasks; the last two computations are task specific. Our results therefore reveal where, when, and how dynamic network algorithms perform different computations over the same inputs to produce different behaviors.A key challenge in neuroimaging remains to understand where, when, and now particularly  Extensive studies revealed that the primate visual system comprises the ventral and dorsal pathways, with specific anatomical and functional hierarchical organization . These pThere is broad agreement that such a bidirectional hierarchical architecture supports much of the information processing that subtends everyday face, object, and scene recognition. However, despite considerable progress, we have yet to understand where, when, and how specific algorithmic computations in the pathways dynamically represent and transform the visual input into integrated features to produce behavior  and viceWe framed this broad problem using the classic logical functions XOR, AND, and OR, in which different algorithms are required to produce correct responses from the same input stimuli . Our analyses of the ensuing computations in the networks of individual participants systematically revealed four distinct stages that represent and transform the same inputs to produce different task-specific behavior. The first two stages of linear computations similarly represent the two inputs across the XOR, AND, and OR tasks. The last two stages of nonlinear computations differently represent the same inputs in a task-dependent manner   . A video inputs)  visualiz inputs)  stage byMethods, MEG Data Acquisition, Source Reconstruction).Starting with behavior, Methods, Representational Patterns). Our analyses reveal that individual MEG source responses reflect changing representations of the visual inputs in the brain, revealing four different stages of neural computations that lead to behavior in each task  in V1-4 regions with onset from ~60ms poststimulus, quantified as the product of and the LEFT/RIGHT representation pattern metric  and the multivariate R2 of a linear model for each binary input , color coded in light blue for the left input, in orange for the right input.Linear discrimination of both inputs (‘LinBoth’) on the same occipital and ventral sources ~ 100ms poststimulus, quantified as the product of the BOTH representation pattern metric  and the multivariate R2 of a linear model considering both inputs with no interaction , color coded in magenta.Nonlinear integration of both inputs (‘NonLin’) for task performance  in temporal-parietal regions ~260ms, quantified as the product of the XOR representation pattern metric  and the significant improvement in model fit with interaction term , color coded in green.Nonlinear integration of both inputs together with response-related activity (‘NonLin&RT’) in postcentral gyrus ~400ms, quantified as mutual information (MI) between the 2D MEG magnetic field and RT on the corresponding trial , also thresholded by the product of the XOR pattern metric and the model interaction term, color coded in yellow.In We next detail the dynamic unfolding of each color-coded computation on single sources, using an exemplar XOR participant  that linearly and jointly represents the ‘on’ vs. ‘off’ state of both inputs takes two distinct forms. In midline occipital sources , all fouThe third and most critical computation that starts distinguishing the task  occurs when the parietal source  nonlineaFinally, the fourth stage on a postcentral gyrus source  and parietal  areas.Here, we addressed the challenge of understanding where, when, and how the brain dynamically implements different algorithmic computations over sensory inputs. We tightly controlled behavior using the simple logical functions XOR, OR, and AND as tasks that require different computations over the same tightly controlled binary inputs. Our analyses revealed, at the level of individual MEG sources, four main stages of computation that dynamically unfold from ~60 to 400 ms poststimulus. The first computation linearly discriminates the ‘on’ vs. ‘off’ state of each input in contral-lateral occipital cortex ~60ms poststimulus. This is followed by the linear discrimination of both inputs on occipital and ventral sources ~100ms, followed by the nonlinear integration of both inputs, revealing the XOR, AND, or OR task solution in 2D source response space in the parietal-temporal regions ~260ms, and finally the nonlinear integration with RT-related activity in postcentral gyrus ~400ms. These four stages are common to XOR, AND, and OR, with the main task-related changes occurring in the latter two nonlinear stages. Notably, we performed all statistical analyses leading to these results within each individual participant, controlling the familywise error rate (FWER) over all considered sources and time points. By treating each participant as an N-of-1 study, 10 participants per task provide 10 independent replications of the experiment. We replicated the four computational stages in at least 9/10 participants , providing strong evidence that a majority of individuals in the population sampled and tested in the same way would show the same effects .Our systems-level approach aims to reverse engineer, from mass brain signals, the hierarchy of brain computations that represent and transform sensory inputs to produce behavior—i.e., the brain’s algorithm of the behavioral task. The four stages of computation that we systematically found in each individual participant and tasks meet the five key properties of an algorithm: (1) the inputs were specified as the four possible combinations of two binary inputs; (2) the output responses were also specified as the responses of the logical functions XOR, OR, and AND; (3) the algorithms were definite in each task, with a sequence of two characterized Lin and two NonLin computations that transformed the same inputs into the task-specific outputs; the algorithms were also (4) effective in the brain, in the sense that they only relied on brain resources, and (5) finite in processing time, producing behavior with ~450–500ms.Note that reverse engineering a systems-level algorithm at the granularity of MEG brain measures does not preclude the existence of different compositions of algorithms at lower levels of granularity of individual neurons that together implement the higher-level algorithm . Rather, such systems-level analysis provides constraints on where (the brain regions) and when (the specific time windows) specific computations take place, enabling targeted studies of the algorithmic ‘how’ across modalities and granularities of brain measures. Generalizing from our case study of the algorithms underlying the simple XOR, AND, and OR functions to more naturalistic face, object, and scene categorization tasks will incur many challenges that we can frame in the context of the properties of an algorithm detailed above.A key challenge is that the task-relevant features of real-world faces, objects, and scenes, may be completely different for different behaviors and participants, effectively changing the inputs to the algorithm. Unfortunately, task- or participant-specific features are generally not considered in studies of neural representation, processing, and categorization. Their understanding remains a similar challenge for deep convolutional neural network research, including instances when these are used as models of the brain. Specifically, a key property of an algorithm is that we specify its inputs as precisely as possible. In real-world categorizations, this implies understanding which specific features of the complex images are task relevant for each particular participant performing each specific behavioral task. Furthermore, specification of the outputs is another key property of an algorithm. Passive viewing, or one-back tasks do not provide this specification. For example, from the same face, the feature of a smiling mouth feature will be used to overtly respond ‘happy’, but the forehead wrinkles to respond ‘45 years of age’; from the same car picture, its specific shape to respond ‘New Beetle’, but the front tyre shape to respond ‘flat tyre’; or the specific roof tiles to respond ‘Chrysler building’ but the density of buildings on the horizon to respond ‘city’; and so forth. Relatedly, experts vs. novices will use different features to classify the same pictures of the 35 different subtypes of sparrows that exist in North America. Such relative perceptual expertise and associated features generally characterize the relationship between visual cognition and outside world stimuli. Then, to infer the hierarchical stages of computation from the brain measures, we can start tracing the dynamic representation of these specific task-relevant input features, when we have formally characterized them, between stimulus onset and explicit output task behavior, as we did here. Different modalities or granularities of brain measures  but well controlled (e.g. with generative systems rather than uncontrolled 2D images), as they are in any algorithm.We recruited 35 participants . All reported normal or corrected-to-normal vision and gave written informed consent to participate in the experiment and for their data to be anonymously published. We conducted the study according to the British Psychological Society ethics guidelines and was approved by the ethics committee at the College of Medical, Veterinary and Life Sciences, University of Glasgow.We synthesized an average face using a generative photorealistic 3D face model  to whichEach trial began with a central fixation cross displayed for a randomly chosen duration (between 500 and 1000ms), immediately followed by one of the four stimulus classes described above and displayed for 150ms. We instructed participants to maintain fixation on each trial, to pay attention to the left and the right lenses and to respond as quickly and accurately as possible by pressing one of two keys ascribed to each response choice with the index or middle fingers of their right hand. Responses were ‘same’ vs. ‘different’ in the XOR task; ‘both dark’ vs. ‘otherwise‘ in AND; or ‘at least one dark’ vs. ‘otherwise’ in OR. Participants were randomly allocated to one of the three tasks.Stimuli were presented in blocks of 80 trials, with random intertrial interval of [800–1300ms] and randomized stimulus order in each block. Participants completed a total of 20–24 blocks split across 2–3 single day sessions, with short breaks between blocks. Each session lasted 2.5–3hr. We selected this recording time and number of trials based on sensitivity of MEG for within-participant whole brain corrected feature representation in previous experiments using face stimuli . We focuWe recorded the participants’ MEG activity using a 248-magnetometer whole-head system  at a 1017 Hz sampling rate. We discarded each participant’s runs with more than 0.6 cm head movement measured with prerun vs. postrun head position recordings. Participants were excluded if the number of trials remaining after preprocessing (eye movement artifact rejection and rejecting runs for excessive head motion) was less than 700. We excluded five participants resulting in a final sample sizes of N = 30 (10 per task). Mean head movement (averaged across blocks) across participants was 0.3 cm .We performed analyses with Fieldtrip  in MATLANext, we epoched the data into trial windows ([−500 to 1000ms]) around stimulus onset, low-pass filtered the data at 45 Hz (third order two-pass Butterworth IIR filter), resampled to 256 Hz, and decomposed using Independent Component Analysis (ICA), separately for each participant. We identified and projected out of the data the ICA sources corresponding to heartbeat and eye blinks or movements (2–4 components per participant).For each participant, we coregistered their structural MRI scan with their head shape recorded on the first session and warped to standardized MNI coordinate space . Using bEvery 4ms time point between –100 and 400ms poststimulus, we computed independent multivariate linear regressions to model the dynamic representation of the state of each input  into the 2D MEG responses of each source. We computed three linear models covering each input separately  and additively.2 that quantifies multivariate variance as the determinant of the covariance matrix:We fitted each model with ordinary least squares, resulting in beta coefficients for the intercept and slope. We quantified the fit in the 2D response space of the source with a multivariate R2 values per source with 4ms resolution.where 2 computation explained above, and extracted the maximum R2 across all sources and time points. This produced a distribution of 100 maximum R2 values, of which we used the 95th percentile as statistical threshold (FWER p<0.05).To control the FWER over all considered time points and sources, we computed a nonparametric statistical threshold with the method of maximum statistics . SpecifiA fourth model considered, for each source and time point the nonlinear interaction term between the left and Right inputs.A log-likelihood ratio (LLR) tested whether the added interaction term significantly improved model fit (p<0.05), FDR corrected over time points and sources .Linear and nonlinear representations of the two inputs into 2D source activity could form a variety of different patterns. To ensure that these patterns corresponded to expectations (e.g. of an XOR solution), we applied two further computations at each source and time point. First, we computed the pairwise Mahalanobis distances as detailed below between the color-coded 2D distributions of single trial MEG activity in response to each input class see . To do sThen, we quantified the geometric relationships between the two-dimensional centroids of the source responses to each input class. We did so by combining the pairwise distances in the way that quantifies the expected representational pattern see , right:left lens representation (LL): mean – mean. This measure contrasts distances where the left lens state changes, with distances where the left lens state does not change.right lens representation (RL): mean – mean. As above, for the right lens.both lenses representation (BL): mean – stdXOR representation: mean – mean. Contrasts the distances between elements of the two different output classes with the distances between elements within each output class.AND representation: mean – meanOR representation: mean – meanth percentiles of these maxima as thresholds .We tested the statistical significance of each pattern with permutation testing, using again the method of maximum statistics. Over 25 permutations, at each source and time point, we randomly shuffled input ‘on’ and ‘off’, repeated the above distance calculations, computed the maximum differences, and used the 952 for linear representation patterns of the left or right input  or with the significant nonlinear LLR test statistic .Finally, we weighted the significant XOR, AND, and OR representation patterns (see above) with the significant R2. For early representation of both lenses, we computed its R2 peak time. Finally, for XOR, AND, and OR nonlinear representations, we also selected the peak times of the respective representation task pattern distance measure. For each state, we then computed time stamps as median across observers and extracted the sourcewise representation averaged across participants at the corresponding time stamp. We plotted these sources back onto glass brains in Nilearn  on the 5107 × 102 (source × time points) space separately in XOR, AND, and OR tasks as follows:First, we transformed each participant’s MEG data into the main computation that each source performs at each time point, by assigning to each source the computation  with highest representation pattern score at this time point . This produced a source × time matrix of strongest source-level computations for this participant. Examination of the data revealed strong regularities of the computations performed in different time windows (e.g. LinLeft early on), though each computation could be performed across slightly different sources of the same region across participants . Across participants, we therefore computed the modal computation at each source and time point, producing one group-level computation matrix per task that we then clustered over time, rather than over source × time, as we next explain.In the task-specific computation matrix, we summed at each time point the number of sources that performed each computation . The resulting 6 (computations) × 102 time points matrix represented the total brain volume of each computation over time in the task.k as the elbow of the within-cluster sums of point-to-centroid distances metric—i.e., as the furthest point from straight line between k = 1–20. In the XOR, AND, and OR tasks, different clusters therefore represent different stages of the full process over time, with different brain volumes of source-level computations .We k-means clustered  each computation matrix from step 2, using the 102 time points as samples and selected k = 5 as a good solution. First, a stage 0, before any computation starts, and then four distinct timed stages with different brain volumes of LinLeft, LinRight, LinBoth, XOR, AND, and OR computations. To visualize each stage, we used the onset time of stages 0, 1, and 2 and the central time point of stages 3 and 4. In each stage, we color coded at voxel level in the small brains the most frequent computation across participants .To test whether the XOR, AND, and OR tasks share stages of computation, we computed the percentage of sources that perform the same computations across any pair of stages. This produced a 12 (3 tasks × 4 stages)-by-12 similarity matrix that compares each stage in each task with all other stages in all other tasks. To test the generalization of computation stages between face and Gabor stimuli, we computed the similarity matrix between the group-level stages in XOR, AND, and OR tasks with faces and the individual participant’s (N = 3) stages in XOR with Gabor stimuli . t>), splitting RTs into four equiprobable bins and using continuous MEG . To this end, we used Gaussian-Copula mutual information We used information theory to quantify the association between RTs and MEG activity, as MI  regardless of stimulus types? This is one of the most fundamental questions in cognitive neuroscience. This MEG study, by combining interesting experimental paradigms and sophisticated signal analyses, demonstrates four serial neural components in different brain regions that correspond to four system-level computations, respectively. In the interests of transparency, eLife publishes the most substantive revision requests and the accompanying author responses.Decision letter after peer review:eLife. Your article has been reviewed by 2 peer reviewers, one of whom is a member of our Board of Reviewing Editors, and the evaluation has been overseen by Chris Baker as the Senior Editor. The reviewers have opted to remain anonymous.Thank you for submitting your article "Different computations over the same inputs produce selective behavior in algorithmic brain networks" 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) Testing the representational generalization across stimulus set. In principle, the logical computation should be independent of the stimuli and would engage similar four-step neural computation in the brain for many types of stimuli. Although the authors demonstrate similar decoding temporal course and patterns for grating and serially presented stimuli , it is important to perform a generalization analysis across different stimuli. Specifically, the classifier obtained for face stimuli in the main experiment could be used to decode computations performed over grating stimuli , for example.2) Clarifying the temporal relationship of the four components and why their alignment over time still supports the four-step conclusion. Moreover, the 3nd component also shows early onset activation. How to reconcile the results with the 4-step computation view? Please see details in the first two comments raised by Reviewer 2.3) Adding analyses to test alternative computational steps, e.g., 3 system-level, 2 system-level for comparison, and clarifying why the proposed 4-step is the best model to characterize the whole process.4) The behavioral relevance analysis was only performed on the fourth component. What about the first three components which should in principle be related to behavioral performance as well? More generally, is there any neural signature that is related to behavior for both linear and nonlinear calculations?5) The authors should collect new data or at least address the possibility of the involvement of eye movement in the four levels of computation.Reviewer #1:This work by Jaworska et al. examined the spatiotemporal correlates of logical computations over the same stimuli in the human brain using MEG recordings. They revealed four neural components that occur in different brain regions and at varied latencies, corresponding to four system-level computations respectively.Overall, it is an important study addressing the most fundamental question in cognitive neuroscience, that is, how, where and when do the basic logic computations  occur in the brain? The study used advanced analysis approaches to address the thorny question and the results are impressively clean and robust and have been replicated for different stimulus sets and conditions.My major concern is the representational generalization across different stimuli. Specifically, the logical computation  in principle should be independent of the employed stimuli  and would thus engage similar neural computation in the brain for different types of stimuli. Therefore, it is important to confirm that the observed logical computation components could be generalized across different stimulus setting .1) If my understanding is correct, the logical computation  should be independent of the stimuli and therefore would engage similar four-step neural computation in the brain for other types of stimuli. Although the authors demonstrate similar decoding temporal course and patterns for grating and serially presented stimuli , I think it is important to perform a generalization analysis across different stimuli. Specifically, we would expect that the classifier obtained for face stimuli could be used to decode computations performed over grating stimuli, for example.2) I am curious how could the authors make sure that the logical computation (based on the binary classification on four types of stimuli) are the genuine calculation in the brain, since it is completely defined in terms of the experimenter's terminology and the subjects might use different strategy. Have the authors considered other alternative computation over the same inputs? Or put in another way, how could the current results be incorporated into or account for previous findings?3) The last component is interesting and shows behavioral correlates, which is important evidence. If my understanding is correct, that component only applies to nonlinear integration in combination with RT. What about the behaviorally related linear computation? Is there a general behaviorally related neural component that is related to both linear and nonlinear computation?Reviewer #2:How our brain dynamically represents and transforms the same visual input into integrated features to produce different behavior remains unclear. To study the dynamic algorithms from mass neural signals, the authors recorded the MEG activity of participants who resolved the classic XOR, OR, and AND behavioral tasks. Using linear and nonlinear representations, they found that source-localized MEG activity progresses through four systems-level computations identified within individual participants. The first two computations similarly represent the inputs across the three tasks; the last two differently represent the inputs in a task-dependent manner. The topic is interesting and timely. The study is elegantly designed and the data statistics are highly significant.To study the dynamic algorithms from mass neural signals, the authors recorded the MEG activity of participants who resolved the classic XOR, OR, and AND behavioral tasks. Using linear and nonlinear representations, they found that source-localized MEG activity progresses through four systems-level computations identified within individual participants. The first two computations similarly represent the inputs across the three tasks; the last two differently represent the inputs in a task-dependent manner. The topic is interesting and timely. The study is elegantly designed and the data statistics are highly significant. I have some comments listed as below.1. For each task, the authors proposed the 4 systems-level stages of computation link stimulus to behavior , according to the different onsets from post-stimulus . However, the time window of the first stage (Lin) is the same as the second stage (LinBoth), namely, 74-117ms. This was also reflected very well in the results from Figure 1B, the "Lin" and "LinBoth" computations have almost the same onsets and peaks, particularly, the peak of the blue line  looks like much latter than the magenta line . I am confused that how the authors distinguished these two stages when they had the same time window and why the authors proposed they were the two different stages rather than the same stage during their computations. More generally, the authors should offer more evidence for the 4 systems-level stages of computation, for example, whether it explained their results better than the 3 systems-level stages or 2 systems-level stages of computation.2. Similar to the first point, for the results in Figure 3, there were several significant peak values in the very early time window (0-60ms) for the third stage ("NonLin"). However, the author only focused on the late time window (~260ms) for this stage. How to reconcile these early peaks (early effects) of the third (late) stage?3. The behavior data and decoding performance lacked in the first three systems-level computations. Functional inferences based on hypothetical computations of MEG data are not convincing.4. I would like to suggest the authors collect eye movement data to address whether eye movements are a possible confound for the difference between the four systems-level computations.5. The legend descriptions are too long and confusing, like figure 2. I would like to suggest the authors label small figures in one figure to make the legend easier to understand by readers. Essential revisions:1) Testing the representational generalization across stimulus set. In principle, the logical computation should be independent of the stimuli and would engage similar four-step neural computation in the brain for many types of stimuli. Although the authors demonstrate similar decoding temporal course and patterns for grating and serially presented stimuli , it is important to perform a generalization analysis across different stimuli. Specifically, the classifier obtained for face stimuli in the main experiment could be used to decode computations performed over grating stimuli , for example.We framed this question in the context of the 4-stage clustering analysis by examining the generalization of the Gabor stimuli using the four-stage model derived from the face stimuli. Specifically, we also clustered the Stages of each Gabor XOR participant (N = 3) , leading to four stages in each, and compared each stage within the similarity matrix of the four computation stages over face stimuli . In each Gabor participant, their first two Linear stages are similar to XOR, AND and OR face participants, whereas their third and fourth NonLinear stages are similar only to the third and fourth stages of XOR face participants. This generalizes to Gabor stimuli the 4-stage computation model derived from face stimuli.2) Clarifying the temporal relationship of the four components and why their alignment over time still supports the four-step conclusion. Moreover, the 3nd component also shows early onset activation. How to reconcile the results with the 4-step computation view? Please see details in the first two comments raised by Reviewer 2.Clarification of Stages 1 and 2. The alignment in time of the first and second stages in the original manuscript is now clearly separated in each task in the revised figures  Directional asymmetries in interhemispheric transmission time: Evidence from visual evoked potentials. Arch Ital Biol 135:169–182.Ipata A, Girelli M, Miniussi C, Marzi CA (1997) Interhemispheric transfer of visual information in humans: the role of different callosal channels. Cerebral Cortex, 11, 4123-4135.Ince, A.A., Jaworska, K., Gross, J., Panzeri, S., van Rijsbergen, N., Rousselet, G. and Schyns, P.G. (2016). The deceptively simple N170 reflects network information processing mechanisms involving feature coding and transfer across hemispheres. Early unexpected peaks. The early peaks of nonlinear computations in Stage 3 (particularly of the OR task) were indeed problematic. We went back to our pipeline, starting with the data of each participant. We noticed in a few participants that an anomaly occurred due to the potential for the task representational distance measure to be noisy, particularly when brain regions had weak representations of the inputs at specific time points. Though the Log-Likelihood Ratio measure of non-linear computation was significant, its effect size was extremely weak compared to the later effects, implying that the task distance was correspondingly less meaningful. We addressed this anomaly by multiplying the statistically significant R2 and LLR (i.e. the effect size for the evidence of the linear and nonlinear relationships) with the statistically significant distance metrics for each computation, to better combine the two measures (and avoid considering distance metrics with very weak effects). This did not change the substance of a 4-stage process but smoothed these anomalies in the plots, as now shown in the key panels of Figures 1 and 3 of the revised manuscript and in 3) Adding analyses to test alternative computational steps, e.g., 3 system-level, 2 system-level for comparison, and clarifying why the proposed 4-step is the best model to characterize the whole process.We thank you and the reviewers for raising these important points. In our methods we described the computations that led to 4 stages, but we did not explicitly test explicitly alternatives numbers of stages . In our revised manuscript, we now explicitly test these alternative numbers of stages to characterize the whole process. We used a simple data-driven k-means clustering analysis over the measures of each computation across time, pooling all participants’ data and performed separately in the XOR, AND and OR tasks. This created a computation space, where clusters represent different stages of the full process over time, with different brain volumes of source-level computations . In this space, the data clustered into 4 stages in each task , with a first null Stage 0 of no computation followed by 4 stages, each characterized with brain volumes of sourcelevel computations . Using in each task the indexes to these clusters shows that each cluster does indeed capture a specific stage of the entire process that fits the 4-stage data presented in Figures 1 and 3 of the original paper. To conclude, 4stage is the better model of the entire process.Furthermore, a similarity analysis of the 4 stages in each task  revealed the similarity of Stages 1 and 2 of linear computations across the XOR, AND and OR tasks , followed by dissimilar nonlinear computations  to compute and XOR, AND and OR for task specific behaviors.4) The behavioral relevance analysis was only performed on the fourth component. What about the first three components which should in principle be related to behavioral performance as well? More generally, is there any neural signature that is related to behavior for both linear and nonlinear calculations?Thank for you for this comment. We had performed this full analysis but did not report it. It is now included in the manuscript  and we can indeed confirm that the fouth Stage is the only one that relates to behavioral reaction time.5) The authors should collect new data or at least address the possibility of the involvement of eye movement in the four levels of computation.This is a difficult point to address for two main reasons: First, we did not collect eye movement data for all participants and so would need to rerun the entire experiment to address this comment. Second, there is no specific proposal for how eye movements could confound the differences between the four systems-level computation stages that we report. Here, we review the arguments that make such confounds unlikely. First, consider that our experimental conditions and task instructions were optimized to minimize eye movement artifacts. Participants were instructed to fixate the center of the screen and not move their eyes throughout the experiment, a fixation cross was presented at the center of the screen on each trial (for a random duration of 500-1000ms), the stimuli were presented for only 150 ms  and preprocessing of our MEG data removed all eye movements artifacts (we now amended the manuscript to make this explicit). Second, further consider that each stage is consistently located in specific brain regions, which are not primarily involved with fixation control . Specifically, we located LinRight and LinLeft at left and right occipital cortex at Stage 1, LinBoth at Stage 2, starting ~15 ms later, in mid-line occipital cortex and right fusiform gyrus, and NonLin XOR, AND and OR task-relevant computations in temporal parietal, parietal and pre-motor cortex. Furthermore, we now show that Stages 1 and 2 are similar across all three tasks, with rapid sequencing (of ~10-15 ms) compatible with the timing of hemispheric transfer , not with the timing of different fixations (each would take ~150 – 300 ms with face stimuli). The last two NonLin computations are different across the three tasks, but they occur between ~200-400 ms, when the stimulus is not on the screen anymore. In sum, as the two inputs are identical in all tasks, but disappear from the screen (at 150 ms), it is unclear how specific eye movements would confound Stages 3 and 4 (~200-400 ms post-stimulus). These two stages perform task-specific nonlinear integrations (of inputs absent from the screen at this time) over 76 ms in temporal, parietal and premotor cortex . In sum, we deem it unlikely that different patterns of eye movement in each task could confound the stages of computation that we report.Neuropsychologia 32:439–448.Brown WS, Larson EB, Jeeves MA (1994) Directional asymmetries in interhemispheric transmission time: Evidence from visual evoked potentials. Arch Ital Biol 135:169–182.Ipata A, Girelli M, Miniussi C, Marzi CA (1997) Interhemispheric transfer of visual information in humans: the role of different callosal channels. Cerebral Cortex, 11, 4123-4135.Ince, A.A., Jaworska, K., Gross, J., Panzeri, S., van Rijsbergen, N., Rousselet, G. and Schyns, P.G. (2016). The deceptively simple N170 reflects network information processing mechanisms involving feature coding and transfer across hemispheres. Reviewer #1:1) If my understanding is correct, the logical computation  should be independent of the stimuli and therefore would engage similar four-step neural computation in the brain for other types of stimuli. Although the authors demonstrate similar decoding temporal course and patterns for grating and serially presented stimuli , I think it is important to perform a generalization analysis across different stimuli. Specifically, we would expect that the classifier obtained for face stimuli could be used to decode computations performed over grating stimuli, for example.We thank the reviewer for this comment. Our analyses are mainly within participant and so we do not have an explicit classifier for the group (and such classifier would likely require higher participant numbers). Thus, we addressed the question of generalization by considering explicitly the similarity of stages of computation across the XOR, AND and OR groups of participants we have, with an approach that also speaks to the generalization from face stimuli to other types of stimuli. See the fuller response in point 3 of the response to the Editors and the generalization data in Figure 1—figure supplement 1.2) I am curious how could the authors make sure that the logical computation (based on the binary classification on four types of stimuli) are the genuine calculation in the brain, since it is completely defined in terms of the experimenter's terminology and the subjects might use different strategy. Have the authors considered other alternative computation over the same inputs? Or put in another way, how could the current results be incorporated into or account for previous findings?Here, we took the simplest computations that we could think of, documented in theory of computation as a well-known nonlinearity required to resolve XOR (in contrast to AND and OR). Each of these tasks are defined completely by a four element “truth table” and theory of computation consider them to be undecomposable computational primitives. We acknowledge the limitations of such simple tasks, but their advantage is that if the participants are correctly performing it, then we can be confident that somewhere in their brains the XOR computation (i.e. the rule specified in the truth table) is indeed being dynamically implemented. It is difficult to see how different strategies than nonlinear integration could be employed to perform XOR starting with two separate inputs. We did indeed ensure (cf. Stage 1) initial contra-lateral representation of each input (as predicted from neurophysiology and neuroanatomy) to enable a network model that takes in two separate inputs and must necessarily integrate them for behavior. From there on, we traced where and when MEG source activity linearly (Stage 2) and nonlinearly (Stages 3 and 4) represents the same two inputs on the way to behavior.As we cannot access the ground-truth computations of the brain, all we can do is model them. With each task  and participant (N = 10 in each group), we now show similar Stages 1 and 2 of linear computations over the two inputs and divergent, task-related Stage 3 and Stage 4 computations over these same inputs . Thus, within each task and across the tasks , we are validating that the brain does indeed perform different computations over the same inputs, both at different stages of the process within task and across the final two stages across tasks.3) The last component is interesting and shows behavioral correlates, which is important evidence. If my understanding is correct, that component only applies to nonlinear integration in combination with RT. What about the behaviorally related linear computation? Is there a general behaviorally related neural component that is related to both linear and nonlinear computation?Thank for you for this comment. We had performed this analysis but did not report it. We have now included in the manuscript  and can indeed confirm that the fourth component is the only one that relates to behavioral reaction time.Reviewer #2:The study is elegantly designed and the data statistics are highly significant. I have some comments listed as below.1. For each task, the authors proposed the 4 systems-level stages of computation link stimulus to behavior , according to the different onsets from post-stimulus . However, the time window of the first stage (Lin) is the same as the second stage (LinBoth), namely, 74-117ms. This was also reflected very well in the results from Figure 1B, the "Lin" and "LinBoth" computations have almost the same onsets and peaks, particularly, the peak of the blue line  looks like much latter than the magenta line . I am confused that how the authors distinguished these two stages when they had the same time window and why the authors proposed they were the two different stages rather than the same stage during their computations. More generally, the authors should offer more evidence for the 4 systems-level stages of computation, for example, whether it explained their results better than the 3 systems-level stages or 2 systems-level stages of computation.We thank the reviewer for this comment, agree with them and have developed a new data-driven analysis that delivers the 4-stage account of the data, rather than fewer or more stages. We presented these results in the context of the overall response to the 5 main points of the Editors.2. Similar to the first point, for the results in Figure 3, there were several significant peak values in the very early time window (0-60ms) for the third stage ("NonLin"). However, the author only focused on the late time window (~260ms) for this stage. How to reconcile these early peaks (early effects) of the third (late) stage?We thank again the reviewer for this comment and for pointing to these peaks that caused confusion.We explained how we addressed the peaks in the response to the Editors and provide a “Before and After”  that han3. The behavior data and decoding performance lacked in the first three systems-level computations. Functional inferences based on hypothetical computations of MEG data are not convincing.Thank for you for this comment. We had performed this analysis but did not report it. We have now included in the manuscript  and can indeed confirm that the fourth Stage is the only one that relates to behavioral reaction time. Please see response to the Editors above for the figure showing the data.4. I would like to suggest the authors collect eye movement data to address whether eye movements are a possible confound for the difference between the four systems-level computations.See response to this point in response to the Editors’ comments.5. The legend descriptions are too long and confusing, like figure 2. I would like to suggest the authors label small figures in one figure to make the legend easier to understand by readers.We agree, have simplified the caption, and added a Supplementary video  that presents the dynamics that Figure 2 illustrated statically. The figures and new video should make the specifics of the different dynamic computations over the same input a lot easier to understand.


Depression and anxiety are common, debilitating psychiatric conditions affecting millions of people throughout the world. Current treatments revolve around selective serotonin reuptake inhibitors (SSRIs), yet these drugs are only moderately effective at relieving depression. Moreover, up to 30% of sufferers are SSRI non-responders. Endocytosis, the process by which plasma membrane and extracellular constituents are internalized into the cell, plays a central role in the regulation of serotonin  signaling, SSRI function and depression and anxiety pathogenesis. Despite their therapeutic potential, surprisingly little is known about the endocytosis of the serotonin receptors (5-HT receptors) or the serotonin transporter (SERT). A subset of 5-HT receptors are endocytosed by clathrin-mediated endocytosis following serotonin binding, while for the majority of 5-HT receptors the endocytic regulation is not known. SERT internalizes serotonin from the extracellular space into the cell to limit the availability of serotonin for receptor binding and signaling. Endocytosis of SERT reduces serotonin uptake, facilitating serotonin signaling. SSRIs predominantly inhibit SERT, preventing serotonin uptake to enhance 5-HT receptor signaling, while hallucinogenic compounds directly activate specific 5-HT receptors, altering their interaction with endocytic adaptor proteins to induce alternate signaling outcomes. Further, multiple polymorphisms and transcriptional/proteomic alterations have been linked to depression, anxiety, and SSRI non-response. In this review, we detail the endocytic regulation of 5-HT receptors and SERT and outline how SSRIs and hallucinogenic compounds modulate serotonin signaling through endocytosis. Finally, we will examine the deregulated proteomes in depression and anxiety and link these with 5-HT receptor and SERT endocytosis. Ultimately, in attempting to integrate the current studies on the cellular biology of depression and anxiety, we propose that endocytosis is an important factor in the cellular basis of depression and anxiety. We will highlight how a thorough understanding 5-HT receptor and SERT endocytosis is integral to understanding the biological basis of depression and anxiety, and to facilitate the development of a next generation of specific, efficacious antidepressant treatments. Depression and anxiety are the first and sixth highest causes of burden of disability worldwide, respectively  have been implicated in the development of affective disorders such as depression and anxiety. GPCRs are common neurotransmitter receptors present on the cell surface throughout the body. GPCRs are coupled to heterotrimeric G proteins consisting of an α, β, and γ subunits which are either activating  or inhibitory (α) of downstream signaling responses. Following ligand (such as neurotransmitter) binding to the GPCR, the α and βγ subunits dissociate from the receptor to activate/inactivate intracellular signaling pathways. Dopamine, serotonin, GABA, cholinergic and glutamate receptors have all implicated in mood and mood disorders and the antidepressant response  transporters are also implicated in the treatment of depression and anxiety, with SSRIs targeting the serotonin transporter (SERT), serotonin noradrenaline reuptake inhibits (SNRIs) targeting both SERT and the noradrenaline transporter (NET) and triple uptake inhibitors targeting all three transporters, preventing the uptake of serotonin, dopamine and noradrenaline into the cell  are GPCRs present on the cell surface. Serotonin binding to 5-HT receptors activates the receptor and induces or inhibits intracellular signaling through G-protein signaling, as detailed below. Further regulation of serotonin signaling is exerted by the serotonin transporter (SERT), which induces cellular uptake of serotonin, removing it from circulation, thereby limiting its availability for signaling  and are responsible for downregulating the serotonin synthesis that occurs in these neurons, while 5-HT1A heteroreceptors are present on non-serotonergic neurons. Developmentally, 5-HT1A autoreceptors help establish the anxiety response  and psilocybin  endocytosis. In receptor-mediated endocytosis, a cell internalizes proteins such as receptors and transporters from the plasma membrane following ligand binding. This modulates the internalized receptor/transporters plasma membrane distribution which in turn alters the availability of the respective receptor/transporter for functional outcomes such as activated signaling cascades  enriched regions of the plasma membrane, initiating clathrin recruitment, leading to formation of a clathrin lattice on the cytoplasmic face of the plasma  enriched regions of the plasma membrane, leading to recruitment of the phosphatase SHIP2. SHIP2 metabolizes PIP3 to phosphoinositidephosphate . Endophilin is recruited to and is concentrated at PIP2 enriched plasma membrane regions, thus forming a primed endophilin patch , phosphoinositide-3-kinase, Src and Ras. Serotonin binding to 5-HT2A activates PLC, PLD, protein kinase C (PKC), adenylate cyclase and cAMP production, endoplasmic reticulum calcium release, calcium channel activation and RhoGTPase activation , and less commonly, protein kinase A (PKA) and PKC  receptor induces transient production of cyclic-AMP (cAMP) at the plasma membrane via G-protein signaling, while also activating β-arrestin binding and receptor internalization. Following internalization, cAMP production is sustained from PTH receptor endosomes   however, peaks from 30 to 90 min following serotonin addition. No colocalisation with lysosomal compartment markers was observed in either cell line  and hippocampal neurons   and heteroreceptors (on non-serotonergic neurons) respond differently to physiological and pharmacological agonists.5-HTceptors) . In both1A-specific antidepressant compounds, highlighting the importance of endosomal signaling through 5-HT receptors in regards to developing novel antidepressants. “Compound 44” induced potent ERK activation, while inducing β-arrestin recruitment to 5-HT1A and cAMP production at a level comparable to serotonin. Intriguingly, compound 44 was an effective antidepressant in mice, indicating that specific 5-HT1A signaling profiles are beneficial for alleviating depression. “Compound 56” potently induced β-arrestin recruitment compared to serotonin or compound 44, and increased ERK activation and cAMP production to a much greater extent than compound 44. Compound 56 also acted as an antidepressant in mice, but also led to serotonin syndrome   with DOI treatment compared to serotonin  receptor and 5-HTs et al.  further in vivo  transcripts have been identified as upregulated in a meta-analysis by Wittenberg et al.  consisti2 catabolism required for scission of the clathrin-coated pit from the plasma membrane. DENND1A knockdown or dominant negative Rab35 over-expression result in endocytic cargoes being trapped in the cell periphery, consistent with incomplete clathrin-coated pit scission from the plasma membrane  is upregulated in the brain and peripheral tissues of depression sufferers (Zhong et al., + sorting endosomes to facilitate cargo transfer to Rab11+ recycling endosomes (Solinger et al., + recycling endosomal compartment (Lee et al., EHD1 is overexpressed in the prefrontal cortex of depression sufferers (Yoshino et al., Rab4B protein expression is decreased in the brains of depression sufferers (Kang et al., + sorting endosomes mature to Rab7+ late endosomes, with cargoes that are not sorted for Rab4/Rab11-dependent recycling retained throughout this sorting to late endosomal transition (Rink et al., + late endosomes can then fuse with lysosomes, leading to cargo degradation. However, cargoes present in Rab7+ endosomes can be spared from degradation by recycling via the retromer/retriever recycling pathways, which bind cargoes in specific cellular circumstances, removing them from the Rab7+ late endosome and delivering them to the trans-Golgi network for recycling (Seaman, Rab5, Rab7, and SNX27 transcripts are upregulated in PBMCs from MDD patients (Wittenberg et al.,  Seaman, .2A contains a PDZ domain (Xia et al., + late endosomes.SNX27 is a member of the sorting nexin family of cargo sorting regulators and plays an integral role in cargo sorting for the retromer complex (Temkin et al., 1A and 5-HT2A, disrupting their typical plasma membrane and endosomal signaling profiles (The genetic, transcriptomic and proteomic changes associated with depression and anxiety detailed above  all coalprofiles , or enhaprofiles , which w1A, 5-HT2A and SERT. Reduced β-arrestin levels would further impair 5-HT1A, 5-HT2A endocytosis, while PKC downregulation would lead to reduced 5-HT2A and SERT phosphorylation, reducing their endocytosis. Reduced 5-HT1A endocytosis could lead to increased plasma membrane signaling, with reduced endosomal signaling, altering the cellular response to serotonin and other potential therapeutic agents. Reduced 5-HT2A endocytosis could similarly impair 5-HT2A endosomal signaling, while reduced PKC phosphorylation could lead to altered receptor phosphorylation patterns and diminished or aberrant responses to serotonin and hallucinogenic therapeutics. Rab4b downregulation would be likely to reduced rapid recycling of 5-HT1A and 5-HT2A, which would impair receptor resensitization following endocytosis, further altering 5-HT receptor signaling outcomes. Reduced SERT CME would likely lead to increased plasma membrane SERT levels, leading to continued serotonin uptake from the extracellular space, reducing the availability of serotonin for signaling. Impaired CME is likely to reduce serotonin signaling through multiple mechanisms.In our hypothetical model, PACSIN3 mutation, PACSIN2 overexpression, DENND1A mutation, DENND1B downregulation, and PICALM overexpression are likely to impair endocytic uptake by CME. This CME impairment could conceivably reduce the endocytosis of 5-HTClearly, there is a possibility that SERT undergoes clathrin-independent endocytosis upon treatment with the SSRI sertraline (Lau et al., Endocytosis is clearly central to the regulation of serotonin signaling, SERT, the therapeutic response to hallucinogens, SSRI function and the function of novel antidepressant compounds. Here we present a hypothetical model of how SNPs, transcriptional and protein expression changes in patients with depression and anxiety could alter the endocytosis of serotonin receptors and SERT. This nascent area of neurobiological research highlights the potential impact of endocytosis in these disorders. Despite their apparent importance in mediating therapeutic effects, it is still unclear whether CME is required for SERT uptake, and 5-HT receptor and SERT-specific endocytic adaptors remain unidentified. Perhaps most startling is the lack of exploration of the effects of endosomal sorting on 5-HT-mediated serotonin signaling, despite clear evidence supporting endosomal signaling mechanisms for how each exert their cellular effects. It is abundantly clear that building a detailed understanding of the endocytic pathways of 5-HT receptors and SERT can enhance our understanding of the biological bases of depression and anxiety. A comprehensive understanding of antidepressants and hallucinogenic functions is critical in facilitating the development of the next generation of more effective antidepressants and anxiolytics and a deeper understanding of the genesis of these disorders.The original contributions presented in the study are included in the article/supplementary material, further inquiries can be directed to the corresponding author/s.ND and GR wrote the manuscript. ND prepared the figures. GR conceived of the manuscript. All authors contributed to the article and approved the submitted version.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


Thymic stromal lymphopoietin (TSLP) was initially demonstrated to be critical in regulating inflammatory responses among various allergic disorders . Although two isoforms (short form and long form) of TSLP have been demonstrated in human tissues, the long form of TSLP (lfTSLP) is strongly implicated in the pathogenesis of allergies and cutaneous immune-mediated diseases. The immunomodulatory activity of lfTSLP varies widely, driving T helper (Th) cells polarizing Th2 and Th17 immune responses and inducing itch. Moreover, lfTSLP is closely associated with skin fibrosis, epidermal hyperplasia, angiogenesis, and homeostatic tolerogenic regulations. This review highlights significant progress from experimental and clinical studies on lfTSLP in cutaneous immune-mediated diseases . We also offer original insights into the pleiotropic properties of the cytokine TSLP in various pathophysiological conditions, with significant clinical implications of TSLP-targeted therapies for immune-mediated skin diseases in the future. Muriin vitro , 4. Whenin vitro . The funin vitro , 7. Huma in 2001 , 9. The  in 2001 , 9. Its  in 2001 , 9. Muri in 2001 , 8, 9. H in 2001 , 11. Dur in 2001 , 12, 13. in 2001 , 12–17.Over the past two decades, experimental and clinical studies have demonstrated TSLP to be a potent initiator of type 2 allergic inflammatory responses in humans and mice . FurtherEmerging data have provided more substantial evidence of a dichotomy between the variants of TSLP in humans: lfTSLP and sfTSLP . SfTSLP in vivo and the mechanism of regulation in the signaling pathways involved in sfTSLP   , 27. Nexvia macrophage-derived and thymus and activation-regulated chemokine production , leading to persistent scratching in AD patients . Physicaannel A1 . A very annel A1 . Moreoveannel A1 , 63.AD treatment has traditionally been confined to topical immunosuppressant use . Systemiin vitro study proved that TSLP and T cell-derived CD40L synergy contribute to DC maturation and subsequent IL-23 production by skin DCs and primary blood obtained from patients with PVs, which can be inhibited by a specific type 2 immune cytokine IL-4 in a dose-dependent manner (Psoriasis vulgaris (PVs) is a chronic immune-related skin disorder substantially detrimental to patients’ QOL . Additiot manner . Severalt manner . Serum Tt manner , 72. Altt manner . The disIn addition to skin inflammation, PVs’ hallmarks include excessive keratinocyte proliferation, impaired differentiation, and epidermal hyperplasia in psoriatic plaque . AlthougIn vitro studies using primary synovial fibroblasts (SFC) derived from PsA patients showed that TSLP was significantly elevated in PsA SFC-conditioned medium (Psoriatic arthritis (PsA), a musculoskeletal inflammation associated with cutaneous manifestations, is a common comorbidity in PVs patients and develops in up to 40% of this disorder , 75. Angd medium . Furtherd medium , 78, 79.Bullous pemphigoid (BP), a highly prevalent type of skin autoimmune subepidermal blistering disease that mainly affects the elderly, typically manifests as pruritic vesicles and bullae . The patin vitro. More importantly, increased expression of TSLP can also be observed in both local skin-specific △NC16A mice and basal keratinocyte-specific △NC16A mice, suggesting that BP180 dysfunction may induce basal keratinocytes to produce TSLP. Nevertheless, how BP180 dysfunction in keratinocytes triggers the release of TSLP and the molecular pathways/interactions in this process require further clarification.Mice models have played a vital role in elucidating the mechanisms of BP . One stuPersistent pruritus is the most bothersome symptom in BP patients and significantly affects patient’s QOL . Given tThe downstream mechanisms of TSLP in BP have received little attention. Li et al. showed that the number of DC-specific intercellular adhesion molecule-3-grabbing non-integrin (DC-SIGN)-positive DCs in BP lesional skin was higher than that in healthy controls and correlated with more robust TSLP expression in the epidermis and blisters of BP patients . In compSystemic sclerosis (SSc), characterized by fibrosis of the skin and internal organs and vasculopathy, is an autoimmune disorder with high morbidity and mortality . It is gin vitro , IL-13, interferons, and TNF superfamily member LIGHT, have been demonstrated to induce TSLP release in SSc , 97. TLRin vitro  and polyin vitro . Moreovet manner . LIGHT ct manner . FurtherIn vivo and in vitro studies further demonstrated that TSLP strongly promoted Smad2 phosphorylation, a canonical TGF-β signaling pathway  is a chronic cutaneous disease that features recurrent itching wheals for more than six weeks without a definite eliciting factor . IL-25, Behçet’s Disease (BD) is one of the most prevalent neutrophilic dermatoses characterized by recurring damage to the mouth, eyes, genitals, and skin . In seveVitiligo, an acquired chronic depigmented skin disorder characterized by white macules’ development, features the loss of functional epidermal melanocytes . The autRosacea is a chronic inflammatory disorder that occurs in the middle of the face and mainly affects facial blood vessels and the sebaceous gland . TSLP exRecently, pathway-based analyses have suggested that numerous potential pathways, including TSLP signaling, could be involved in the immunopathogenesis of systemic lupus erythematosus (SLE). SLE could also present as a mucocutaneous manifestation and can be only involved in the skin, as in cutaneous lupus erythematosus , 115. HoAlopecia areata (AA) is an autoimmune disorder with a lifetime incidence of nearly 2% . In addiTSLP is a critical regulator initially characterized by its ability to link innate immunity to type 2 acquired immune response at barrier surfaces. Emerging data show that TSLP can indirectly or directly affect a variety of non-immune and immune cells involved in a broad array of cutaneous immune-mediated disorders beyond just AD, vastly widening the function of TSLP in different skin conditions.A group retrospectively reviewed the samples of eosinophil-rich dermatoses and identified that TSLP was diffusely expressed in arthropod assault, drug rash, AD, BP, and non-BP eosinophilic spongiosis . Based oThe manuscript was written by S-HW with significant contributions from Y-GZ. All authors contributed to the article and approved the submitted version.This study was supported by the National Natural Science Foundation of China (grant number 81972944) and the Beijing Natural Science Foundation (grant number 7192166).The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


The cGAS-STING signaling pathway is an autoimmune inflammatory pathway that can trigger the expression of a series of inflammatory factors represented by type 1 interferon. Recent studies have found that the cGAS-STING signaling pathway played a significant role in liver physiology and was closely related to the progress of liver diseases. For example, activating the cGAS-STING signaling pathway could significantly inhibit hepatitis B virus (HBV) replication in vivo. Moreover, the cGAS-STING signaling pathway was also closely associated with tumor immunity in hepatocellular carcinoma (HCC). This review summarized the role of the cGAS-STING signaling pathway in several common liver diseases, especially the current application of the cGAS-STING signaling pathway in liver disease treatment, and prospected its future research, which provided a new idea for understanding and treating liver diseases. Liver disease is a major cause of illness and death, affecting many people worldwide. Liver diseases mainly include viral hepatitis, alcoholic liver disease (ALD), non-alcoholic fatty liver disease (NAFLD), and related liver cirrhosis, liver failure (LF), and hepatocellular carcinoma (HCC). Liver disease is prevalent worldwide with a poor long-term clinical prognosis, leading to severe public health problems and potentially threatening a large part of the global population.Cyclic guanosine monophosphate–adenosine monophosphate (GMP-AMP) synthase (cGAS), considered as a pattern recognition receptor (PRR) and direct cytoplasmic dsDNA sensor, was discovered by Dr Chen and his group in 2013. When cGAS binds to dsDNA, the cGAS-STING signaling pathway is activated and then induces the expression of type I IFNs and other inflammatory cytokines, triggering innate immune responses . As STINRecently, researchers found that the cGAS–STING signaling pathway was closely related to the occurrence and development of multiple liver diseases. Therefore, this review summarized the regulatory functions and mechanisms of the cGAS–STING signaling pathway in various liver diseases. Based on this, the therapeutic value of this signaling pathway in liver diseases was also discussed.Liver diseases include viral hepatitis, ALD, NAFLD, and related liver cirrhosis, LF, and HCC. Liver disease has a wide range of effects all over the world. About 2 million deaths occur each year, of which 1.4 million dies of liver cirrhosis and 0.6 million dies of viral hepatitis and HCC. Liver fibrosis and HCC are the 11th and 16th world death causes, respectively. Taken together, they account for 3.5% of total global deaths.Furthermore, these statistics are not always accurate because specific death statistics are sparse in many regions, such as Africa. Meanwhile, accurate mortality data are not available for approximately one-third of countries worldwide . TherefoFrequently, liver diseases are caused by cellular oxidative stress and inflammatory response, leading to excessive extracellular matrix (ECM) production. ICM collagen-dominated ECM deposition destroys the liver’s normal structure, leading to liver dysfunction, followed by fibrosis, cirrhosis, and hepatocellular carcinoma. Specifically, following the onset of hepatocyte injury, cytokines and proteins rapidly increase and initiate inflammatory responses . The devcGAS has been identified as a cytosolic pattern recognition receptor and a cytoplasmic DNA sensor that can activate the IFN pathway. cGAS is activated by mislocalized DNA from different sources in the cytoplasm, such as viruses, bacteria, protozoa, mitochondria, and self-DNA from tumors or dead cells . Nuclear2+ sensor stromal interaction molecule 1 (STIM1) in the resting state  located in the endoplasmic reticulum (ER) with four possible transmembrane domains . Normallng state . When cGng state . cGAMP ang state . Activatng state . As we kimmunity . Concurrimmunity  , hepatitis B virus (HBV), hepatitis C viruses (HCV), hepatitis D virus (HDV), and hepatitis E viruses (HEV). According to the global hepatitis report released in 2017, 1.4 million people died of viral hepatitis infection. Most deaths from viral hepatitis are due to hepatitis B and hepatitis C. It is estimated that 257 million people live with HBV, and 71 million people live with HCV worldwide . HoweverALD affects more than 150 million people and remains the leading cause of liver-related mortality worldwide . In 2010Additionally, the rising obesity rate worldwide has also increased liver injury risk caused by NAFLD and non-alcoholic steatohepatitis (NASH) . Accordi+ T cells, which induce systemic antitumor immunity to control local and distant tumor growth . Recently, it is found that the cGAS-STING signaling pathway was involved in the occurrence and development of HCC. In some intense states, cancer cell’s nuclear and mitochondrial DNA is leaky as micronuclei, chromatin fragments, or free telomeric DNA . DNA damr growth   cells and CTLs. Recently, several novel immunotherapeutic methods have been used in clinical trials for the treatment of HCC, including immune checkpoint inhibitors, new types of immune cell adoption , and microRNAs . HoweverIn addition to several common liver diseases summarized above, the current studies suggest that the cGAS-STING signaling pathway may also involve autoimmune liver diseases. The cGAS-STING signaling pathway can trigger autoimmune diseases by producing type I IFN by recognizing the DNA accumulated in the cytoplasm, and inhibiting the activation of this signaling pathway can slow the progression of diseases.As mentioned earlier, IFN is essential for mediating acquired immunity and the antitumor immune response. However, cGAS is essential for the expression of IFN-β, as proved by the fact that cGAS knockout mice cannot release type I IFN after detection of cytoplasmic DNA . STING rAs mentioned above, cGAS-STING signaling pathway activation has a remarkable inhibitory effect on liver viral infection and cancer, thus becoming an exciting target in the field of liver disease immunology and oncology in recent years. Pharmacological modulation of the STING, which is well characterized both structurally and functionally, plays an important role in various liver diseases and is a promising drug target. Currently, the clinical application research of the cGAS-STING signaling pathway is focused on the gene and protein levels.Advances in therapeutic research at the gene level have focused on hepatitis virus, particularly HBV infection. HBV is like a kind of invisible virus, which cannot trigger any innate immune response. In human hepatocytes, exposure to the naked HBV gene will activate innate antiviral immune responses. However, in the process of HBV infection, it is not detected to a large extent, which may be due to the packaging of DNA in the viral capsid. Another explanation for this lack of sensing is the absence of functional STING protein in hepatocytes. The basal expression of cGAS has the activity of anti-HBV infection, including reducing the cccDNA of the virus.in vivo transfection of mice hepatocytes resulted in about one-third of cells expressing STING, which was sufficient to detect HBV infection and further reduce the viral load in the organ. Since the virus has not evolved to interfere with DNA sensing actively, liver-directed STING gene transfer combined with cGAMP therapy may be a novel method for treating HBV infection. Although the potent cGAS-STING signaling pathway in hepatocytes may produce harmful results over time, transiently elevating the expression level of this signaling pathway can enable a local antiviral IFN response (Moreover, in the study of transfection, the activation of the cGAS-STING signaling pathway could impair HBV replication and assembly of HBV. Given its antiviral function, cGAS is a preferred target to solve the problem of HBV escaping immune responses. HBV can inhibit the expression of cGAS and its related genes such as MB21D1, TMEM17, and TBK1 . Furtherresponse .Much focus has been on developing targeted agents for clinical applications at the protein level, including agonists and inhibitors. Accumulating evidence suggests key proteins in the cGAS-STING signaling pathway as potential drug targets, and relevant agonists could fundamentally improve the efficacy of current cancer treatments, including surgery, chemotherapy, radiotherapy, and immunotherapy . As we kFurthermore, the cGAS-STING signaling pathway is also a critical step in the immune response of HCC to radiotherapy. Exogenous DNA damage induced by radiotherapy can produce cytoplasmic DNA in HCC cells, which enters DCs to activate the cGAS-STING signaling pathway and induce the production of type I interferon. When the cGAS-STING signaling pathway is activated by exogenous cGAMP, it can promote the effect of radiotherapy . In addiIn addition, ER stress and autophagy also serve as barriers to the antitumor effects of STING agonists. They participate in advanced cancer progression by enabling cancer cells to survive under stressful conditions, particularly the ER stress response endowing metastatic cancer cells with the potential for immune evasion . NeverthIn conclusion, the cGAS-STING signaling pathway represents a highly promising direction of research in the field of liver diseases, especially viral liver infections and cancer therapy, both at the genetic and protein levels, which shed new light on the treatment of liver disease.The cGAS-STING signaling pathway was first reported in 2013 by Professor Zhijian Chen’s team. As an important pathway in initiating innate immunity, clinical applications and translational studies of the cGAS-STING signaling pathway have been hotspots in medicine and even life sciences. This section will prospect the future research and application of the cGAS-STING signaling pathway based on the latest research achievements and technologies in related fields.In recent years, studies showed that the intestinal and hepatobiliary tract exhibited host-specific symbiotic colonization, and the colonized microflora played a key role in the occurrence and development of intestinal and liver diseases. The gut microbiota is essential for maintaining an intact intestinal barrier and controlling the metabolic function of the liver . The gutvia DCs, and thus we have reason to believe that combining cGAS-STING signaling pathway activators with PD-1/PD-L1 specific blockade will certainly greatly improve the efficacy. Moreover, drug targeted delivery has also become a research hotspot at the intersection of medicine and materials science over the past decade. Scientists have utilized nanocarriers to achieve drug-directed delivery, which could decrease the amount of drug required to improve the therapeutic index, reduce the occurrence of systemic toxicity, prolong drug release time for several days after a single administration, and enhance selective targeting to liver cancer cells (3O4 nanoflowers. This nano delivery system could successfully activate cGAS-STING signaling pathway mediated immunotherapy, and the synergistic chemotherapeutic drug exhibited good antitumor efficacy while also exhibiting great performance against distant tumors (In addition, the cGAS-STING signaling pathway can be applied to many cutting-edge clinical therapies as well. As we know, specific blockade of the PD-1/PD-L1 inhibitory pathway is one of the most important strategies for targeted therapy of HCC, and the basis for the implementation of this therapeutic strategy is the infiltration of immune cells in tumor tissues . As menter cells . For exat tumors .In conclusion, the cGAS-STING signaling pathway is at the forefront of medical and life science research and closely related to other cutting-edge research results in this field, which certainly represents a promising research direction with a bright future.As an autoimmune inflammatory pathway, the cGAS-STING signaling pathway triggers an inflammatory storm associated with autophagy, immune escape, and mitosis. Accumulating evidence has implicated the important role of the cGAS-STING signaling pathway in the pathogenesis of multiple liver diseases. In viral hepatitis B and HCC, the cGAS-STING signaling pathway plays an inhibitory role during disease progression, while in ALD and NAFLD, it plays a promoting role. As a newly discovered pathway, it has broad clinical application prospects. Especially in the treatment of viral hepatitis B and HCC, the activation of this pathway can significantly improve the efficacy of existing treatments. In conclusion, the cGAS-STING signaling pathway is closely associated with liver disease progression and represents a significant potential direction for therapeutic research.