Within the primate visual system, areas at lower levels of the cortical hierarchy process basic visual features, whereas those at higher levels, such as the frontal eye fields (FEF), are thought to modulate sensory processes via feedback connections. Despite these functional exchanges during perception, there is little shared activity between early and late visual regions at rest. How interactions emerge between regions encompassing distinct levels of the visual hierarchy remains unknown. Here we combined neuroimaging, non-invasive cortical stimulation and computational modelling to characterize changes in functional interactions across widespread neural networks before and after local inhibition of primary visual cortex or FEF. We found that stimulation of early visual cortex selectively increased feedforward interactions with FEF and extrastriate visual areas, whereas identical stimulation of the FEF decreased feedback interactions with early visual areas. Computational modelling suggests that these opposing effects reflect a fast-slow timescale hierarchy from sensory to association areas.DOI:http://dx.doi.org/10.7554/eLife.15252.001 In humans, the parts of the brain involved in vision are organized into distinct regions that are arranged into a hierarchy. Each of these regions contains neurons that are specialized for a particular role, such as responding to shape, color or motion. To actually ‘see’ an object, these different regions must communicate with each other and exchange information via connections between lower and higher levels of the hierarchy. However, it remains unclear how these connections work.A brain region called the primary visual cortex is the lowest level of the visual cortical hierarchy as it is the first area to receive information from the eye. This region then passes information to higher regions in the hierarchy including the frontal eye fields (FEF), which help to control visual attention and eye movements. In turn, the FEF is thought to provide ‘feedback’ to the primary visual cortex.Cocchi et al. examined how the FEF and primary visual cortex communicate with the rest of the brain by temporarily inhibiting the activity of these regions in human volunteers. The experiments show that inhibiting the primary visual cortex increased communication between this region and higher level visual areas. On the other hand, inhibiting the FEF reduced communication between this region and lower visual areas.Computer simulations revealed that inhibiting particular brain regions alters communication between visual regions by changing the timing of local neural activity. In the simulations, inhibiting the primary visual cortex slows down neural activity in that region, leading to better communication with higher regions, which already operate on slower timescales. By contrast, inhibition of the FEF reduces its influence on lower visual regions by increasing the difference in timescales of neural activity between these regions.The next step is to determine whether similar mechanisms regulate changes in the activity of neural networks outside of the visual system.DOI:http://dx.doi.org/10.7554/eLife.15252.002 The primate visual system is a hierarchical network of feedforward and feedback connections that supports visual perception, object recognition and selective attention . At the Functional interactions between early sensory regions and later association areas have been investigated in humans using task-based functional magnetic resonance imaging (fMRI) . This woDespite evidence for dynamic bidirectional interactions between FEF and early visual cortex during task-related visual processing, numerous findings from resting-state fMRI (rsfMRI) studies, in both human and non-human primates, suggest there is little or no functional coupling between these regions in the absence of active task demands  . At restHere we combined rsfMRI and non-invasive brain stimulation to examine the causal influence of perturbations of local neural activity within early and late visual areas – specifically areas V1/V2 and FEF – in the absence of visual task demands. Across two separate imaging sessions, we employed continuous theta-burst TMS  to inhibTwenty-three healthy adult volunteers participated in two combined TMS-rsfMRI experiments, conducted on separate days. The number of participants was decided based on previous work adopting a similar experimental design . A schemprior to focal stimulation. These analyses quantified the correlation between the blood-oxygen-level dependent (BOLD) signal timecourse extracted from the seed ROIs and the timecourses extracted from all other brain voxels . The ROIs were centered on coordinates within the right occipital pole and the right FEF, as specified above.To characterize the intrinsic connectivity profiles of the two seed regions-of-interest  at baseline, we conducted seed-to-whole brain functional connectivity analyses of the resting-state data acquired As shown in Neural activity in the right FEF seed was positively correlated with activity in bilateral supplementary motor areas, medial dorsal cingulate, bilateral dorsolateral prefrontal cortex, bilateral superior parietal cortex, and extrastriate visual areas including bilateral precuneus, middle occipital gyri, and inferior temporal gyri  Figure , and relNote that the aforementioned baseline effects were replicated using different sized TMS-seed regions see , baselinHaving established baseline patterns of whole-brain connectivity for the two seed regions at rest, we next examined the influence of local inhibitory TMS on this network activity. To this end, we compared patterns of functional connectivity before and immediately after application of TMS over the right occipital pole and FEF target sites .Inhibitory TMS of right visual cortex V1/V2) resulted in the emergence of positive correlations between this region and bilateral FEF . TMS of  resultedWe also assessed changes in functional connectivity pre- and post-TMS (for both V1/V2 and FEF sessions) using a control region ipsilateral to the TMS stimulation site but outside the networks of interest . These analyses revealed no significant changes in functional connectivity between the pre- and post-TMS scans, for both V1/V2 and FEF sessions.It is well known that the local effects of TMS can vary considerably across participants . To inveThere was a significant correlation between TMS-induced modulation of the amplitude of slow signal fluctuations (ALFF) and TMS-induced changes in functional connectivity for the right FEF  and for the right occipital pole  . This anno significant correlations between changes in the amplitudes of slow signal fluctuations  extracted from homologous visual areas in the left hemisphere  and changes in connectivity . Finally, there were no clear associations between changes in ALFF and individual TMS target sites  hemisphere. There were et sites .To further explore the nature and directionality of TMS-induced perturbations in functional connectivity, we employed stochastic dynamic causal modelling (DCM) . DCM conincreased feedforward interactions between this cortical area and higher visual cortical areas. Conversely, reduced positive correlations following FEF stimulation were driven by a reduction in feedback connectivity . Based on evidence from empirical and computational work  of cortical regions not relevant for our hypotheses – in this instance areas in the vicinity of the right postcentral gyrus and left superior temporal gyrus – could also produce effects on functional connectivity similar to those observed after simulated inhibition of V1/V2 or FEF. Second, we tested whether inhibition of these two control regions would cause specific changes in functional connectivity between early visual cortex and FEF. Simulated inhibition of either control region yielded a markedly different change in connectivity than that observed following simulated inhibition of V1/V2 or FEF see . MoreoveWe examined dynamic reconfigurations of intrinsic interactions between cortical areas at lower and higher ends of the visual hierarchy in humans following local stimulation with TMS. We applied inhibitory theta-burst stimulation via TMS to one of two regions – early visual cortex and FEF – within the right hemisphere, and examined changes in functional and effective connectivity in the resting-state. Local inhibitory TMS of these regions generated opposing patterns of connectivity between early and late areas within the cortical hierarchy. These distinct effects are consistent with an intrinsic timescale-based cortical hierarchy , with eaanticorrelated with activity in the FEF bilaterally. Previous neuroimaging work in humans and monkey has suggested little or no functional coupling between FEF and V1/V2 in the resting state  fluctuations may be a marker of TMS-induced modulation of widespread cortical connectivity.The dynamic integration of information between sensory and association regions of the cortex is essential for normal brain function. Here we combined functional brain imaging, neural stimulation and computational modelling to elucidate the neural mechanisms that support the emergence and dissolution of interactions between cortical regions within the human visual system following local perturbations in neural activity. Our results suggest that the selective effect of local inhibitory TMS on diffuse patterns of connectivity can be accounted for by an intrinsic hierarchical ordering of cortical timescales .The study was approved by The University of Queensland Human Research Ethics Committee. Written informed consent was obtained for all participants.Target regions for inhibitory theta-burst TMS were defined using high-resolution structural T1 3D images obtained for each participant and loaded into an ANT Visor Neuro-navigation system with NDI Polaris Spectra infrared camera. The two TMS target sites were identified prior to the first experimental session . The anaA continuous theta-burst TMS protocol was utilized to induce local inhibition of cortical activity using a previously validated protocol . The inhabductor pollicis brevis (APB) muscle) was established. MEPs were recorded using surface electromyography (EMG) electrodes (Ag-AgCl) from the left APB. Electromyography signals were amplified (x1000) and filtered (5–500 Hz) using a Neurolog system , and digitised (20 kHz) using a data acquisition interface  and custom Matlab software . Whole brain T2* images were acquired using an echo-planar imaging sequence . T1 3D images were acquired using the following parameters: 192 axial slices, slice thickness = 0.9 mm; in-plane resolution = 64 × 64, time repetition = 1900 ms, flip angle= 9°, time echo = 2.32 ms, FOV = 230 × 230 mm.Preprocessing of the resting-state fMRI data was performed using the Data Processing Assistant for Resting-State fMRI 3 . The fir20 = 1.22, p=0.23, FEF TMS session: t20 = 0.941, p=0.35). Nevertheless, to ensure that this methodological decision did not alter the main results reported here, we re-ran the analysis incorporating regression of the mean BOLD signal, and obtained similar results.Supplementary analyses demonstrated that both the mean frame-wise displacement  and the Seed-to-voxel correlation analyses were conducted to examine patterns of functional connectivity between seed ROIs (TMS target sites) with voxels in the rest of the brain. We first extracted the mean timeseries across grey matter voxels within a quasi-spherical grey matter seed (radius ~7.5 mm) centered on the group-level coordinates in the right occipital pole  and right FEF . Note that all of the results presented in the main text were obtained using the grey matter ROIs with a radius of 7.5 mm. While this ROI dimension was found to best encompass the participants’ targeted brain areas in standard MNI space, the main findings were also replicated using additional ROIs with radii of 10 mm and 15 mm; see one-sample t-tests, for both experimental sessions  using Neuroimaging analysis: Seed-to-voxel analysis) using bivariate Pearson’s correlations.The amplitude of low frequency fluctuations  θ corresponding to the oscillator of each region i is given by (We modelled slow (0.01–0.1 Hz) fluctuations of the BOLD signal in  regions  as oscil regions  and slow regions  corticalgiven by ,(1)dθidtW (where matrix W .The distribution of s), = max(s). This distribution is depicted in ωFEF = 0.0782, ωV1/V2 = 0.0925, and the whole brain mean = 0.0883 were chosen to approximate the frequency of slow resting-state BOLD fluctuations. The parameters where W used in the Kuramoto model – The representative connectivity matrix λ) to closely match the baseline functional connectivity of the two stimulated regions  with the rest of the brain using the 513-region brain parcellation. To this end, we calculated single-subject 513 x 513 connectivity matrices using the previously preprocessed data (see Preprocessing of neuroimaging data section for details). We then performed 500 simulations for a period of 8 min (discarding 2.12 min of transient time) using the aforementioned model with random initial conditions for varying λ values. We then selected and fixed λ (=0.0028) for consistency, and verified that small variations did not unduly affect the dynamics. The simulated trials that best replicated the functional connectivity in the real data were selected for each participant. The final correlation between data and simulations for the average functional connectivity between V1/V2 (FEF) and the rest of the brain was r = 0.55 (0.45).We started by calibrating the model . These simulations reproduced the topology of changes in functional connectivity between the two stimulation sites – V1/V2 .Summary of manuscript:The authors combine fMRI, TMS, and computational modeling to investigate interactions between visual cortical regions. They find that cortical stimulation of posterior visual cortex (V1/V2) leads to an increase in BOLD correlations with FEF, but stimulation of FEF leads to a decrease in BOLD correlations with posterior cortex. The modeling work indicates that these changes are related to the intrinsic timescales of these regions. This is a truly impressive study. The multi-methodology approach and research question is very novel and the results have important implications for understanding the neural processes facilitating interactions along the cortical hierarchy.Summary of reviews:While all reviewers of the work were quite positive, they raised a number of concerns that should be addressed in a revision. Although the journal policy is usually only to provide a summary of the main points, given that the reviewers raised a number of important and thoughtful points that may be "lost in translation" by condensation, the full reviews are appended below. The Essential Changes are based on the post-review discussion amongst the reviewers and editor and are outlined below. We also strongly recommend the authors review the full list of suggestions from the reviewers and, at their discretion, determine whether they can improve the manuscript based on the constructive criticism provided. The editor did not think that suggested changes requiring collection of new data were essential, though there are some cases where additional analyses of the extant data could be beneficial in addressing the suggestions.Essential changes:1) Most imperatively, two reviewers noted that there is some confusion between the concepts of anticorrelation and connectivity changes that is challenging for the interpretation of the results.Reviewer #3 states, "Positive and negative correlations are not synonymous with increased and decreased connectivity. The authors summarize their findings as "stimulation of early visual cortex selectively increased feedforward interactions with FEF" and " stimulation of FEF decreased feedback interactions with early visual areas." TMS of FEF resulted in a stronger anti-correlations with V1/V2. An argument could easily be made that connectivity increased in both cases, especially since these areas appear to be moderately anti-correlated at rest (see below)."Similarly Reviewer #2 points out that the interpretation of connectivity changes depends not solely on changes to correlation values but the magnitude of the r value with respect to no correlation (r=0).This must be clarified with more careful wording, unpacking of the specific effects  and bett2) Two reviewers (#2 and #3) raised questions about the localization of the TMS sites. This must be clarified.3) Reviewer #1 raised concerns about the accuracy of the structural connectome on which the Kuramoto model is based. The other two reviewers agreed this was a concern so this must be discussed.4) The Reviewing Editor raised a concern about the asymmetry in connectivity between FEF and V1/V2 at baseline and the other reviewers agreed this should be addressed. Specifically, the data in Recommended considerations5) Two reviewers agreed the manuscript would be strengthened by the inclusion of control sites in the connectivity analysis while a third reviewer disagreed. The most useful additional analysis that the majority agreed would be worth investigating would be to examine the connectivity of the V1/V2 site after FEF stimulation and vice versa. We suggest the authors check this out and see if it adds any value to the paper (and include it if it does or provide a brief summary in the reply letter if it doesn't).Reviewer #1:This paper presents a combination of TMS with different analyses of functional/effective connectivity and computational modelling, using a whole-brain formulation based on the Kuramato model. This combination is so far unique and enables the authors to provide evidence for a plausible and quite fundamental principle of cortical organisation, i.e., that cortical areas at different hierarchical levels operate at different time scales. I think this is a strong and innovative paper which elegantly combines complementary techniques. However, from my perspective, a few issues would benefit from clarification or reformulation:1) I think the presentation of the key findings and the conclusions drawn needs to be improved. In brief, the key conclusion seems to be: inhibitory TMS leads to a local slowing of the timescale (frequency of dominant oscillations) at which an area operates; TMS of V1/V2 decreases and TMS of FEF increases the discrepancy in oscillatory frequencies between these two levels of the visual hierarchy; this explains why the former leads to increased bottom-up connectivity and the latter leads to reduced top-down connectivity. The authors get to this on fourth paragraph in the Discussion, but do not make it as explicit as I think they should. Moreover, it would help if this interpretation and the underlying hypothesis would be pointed out more clearly earlier in the paper, e.g. in the Introduction.2) To support this conclusion, it would be helpful if not only the correlation between changes in ALFF and changes in connectivity were shown; additionally, it would be important to see the magnitude and direction of the local changes in ALFF, separately at both levels of the hierarchy, that are induced by TMS.3) One potential concern for the whole-brain Kuramato model is the accuracy of the structural connectome on which the model rests. This connectome was derived from diffusion-weighted imaging data, using probabilistic tractography. It would be helpful if the authors could provide some reassurance that these connectivity estimates are robust and not overly affected by methodological problems of tractography, such as the known bias in reconstructing short- versus long-distance connections.4) Testing the robustness of the functional connectivity results across a variety of preprocessing strategies for resting state data is a strong aspect of this paper. However, it did not entirely get clear what motion correction procedure the results eventually reported are based on. It would be helpful if this could be clarified.Reviewer #2:The aim of the study was to explore long-range connectivity changes (assessed by resting-state fMRI) resulting from a local decrease in excitability induced by continuous theta burst TMS (cTBS). The authors used cTBS to modulate excitability in the V1 and the R-FEF in separate sessions. Inhibition of V1 with cTBS led to an increase in functional connectivity between V1 and R-FEF. Inhibition of R-FEF led to a decrease in functional connectivity between R-FEF and V1. This is an interesting study that looks to combine brain stimulation and neuroimaging with modelling to address a clearly articulated question.Major Comments:1) The authors suggest that there is a significant relationship between the amplitude of local BOLD signal within the FEF and changes in connectivity between FEF and V1 . Could tThis is a difficult problem to address, and I admit I do not have an easy solution, but it should be at the least discussed. I admit that I do not understand the sentence "the effects remained after controlling for changes in mean BOLD signal between baseline and post-TMS sessions", which may be aimed at addressing this conflict.2) The authors present no controls for their study. There are a number of questions raised by this:a) Can they demonstrate that the fluctuations in connectivity between V1 and FEF, separated in time as these scans were, cannot explain the changes here ? It may be possible to get this data from existing datasets – a step towards this would be to study the differences between the two baseline sessions, though this would not account for changes due to differing time spent in the scanner.b) Are these changes specific to the stimulated regions? If the seeds for the functional connectivity analyses are placed elsewhere in the functional networks, can similar changes be elicited? And similarly for regions outside the networks? The authors have the data to perform these analyses.c) Are the changes seen specific to stimulation at these sites? If the network was stimulated at a different site, would this result in the same pattern of results (i.e. is this just a reflection of some perturbation to the network as a whole or does it reflect the specific connectivity between these regions). This would require more data to be acquired, but is important for their conclusion that these are specific effects. A step towards this might be to show that stimulation of the FEF does not lead to connectivity changes in the V1 seed and vice versa, which they have the data to demonstrate.3) One of the major things I struggled with in this study is the authors' interpretation of ante-correlations. To me, the demonstration of a significant, negative functional connection between two regions is not a lack of a functional relationship, a point with which the authors seem to agree. Therefore, a numerical increase in the r value between two regions is not an increase in connectivity if it does not go through 0, but rather is a decrease in (inhibitory) connectivity. If it does go through 0 it is then a reversal of negative to positive functional connectivity and the interpretation is very different.This is an important point of interpretation, and is currently not clear in the manuscript. If I understand correctly, the authors suggest that inhibitory TMS to V1 led to an increase in connectivity between V1 and FEF. These regions were negatively correlated at baseline . Does thLikewise, inhibitory TMS to the right FEF led to a significant decrease in functional connectivity between this region and V1. Does this mean that there is now a significant functional connectivity between these regions, where there was not previously?Reviewer #3:The authors combine fMRI, TMS, and computational modeling to investigate interactions between visual cortical regions. They find that cortical stimulation of posterior visual cortex (V1/V2) leads to an increase in BOLD correlations with FEF, but stimulation of FEF leads to a decrease in BOLD correlations with posterior cortex. The modeling work indicates that these changes are related to the intrinsic timescales of these regions. This is a truly impressive study. The multi-methodology approach and research question is very novel and the results have important implications on the neural processes facilitating interactions along the cortical hierarchy. The manuscript is well written. Several aspects of the data that could affect the interpretation of the results need clarification. Specifically, the authors should evaluate potential influences of the underlying functional organization on the observed interactions, and address the differences between the imaging and modeling results. In addition, I have concerns on the specificity of the localization for both the imaging data and TMS. Conceptually, the authors also need to better address the relation of positive/negative BOLD correlations to increased and decreased connectivity.Main Comments:Positive and negative correlations are not synonymous with increased and decreased connectivity. The authors summarize their findings as "stimulation of early visual cortex selectively increased feedforward interactions with FEF" and "stimulation of FEF decreased feedback interactions with early visual areas." TMS of FEF resulted in a stronger anti-correlations with V1/V2. An argument could easily be made that connectivity increased in both cases, especially since these areas appear to be moderately anti-correlated at rest (see below).Are the observed interactions between V1/V2 and FEF generalizable to posterior visual vs. higher-order  regions? The modeling certainly appears to predict generalizable effects, but I'm not sure the imaging data specifically demonstrate this.Could the correlation patterns be related to the underlying topographic organization? Anatomical and functional studies in monkeys have shown that FEF and visual cortex is topographically organized with foveal and peripheral V1/V2 connected to differentiable portions of FEF . The particular increase/decrease in BOLD co-fluctuations may reflect where stimulation was applied relative to the functional organization of these regions . This would still be an interesting result, but should be clarified.Given that TMS to FEF results in decreased BOLD activity in foveal V1, but increased BOLD activity in peripheral of V1 , were increases in BOLD correlations observed in peripheral V1/V2 for Post-FEF-TMS? Were any decreases in correlations observed in frontal cortex for POST-V1-TMS? If so, would this be predicted by the model?How precise was the TMS? Judging by the provided MNI coordinates, it's not clear that the closest gyrus on the surface is still within V1/V2. It's very close to lateral occipital extrastriate areas such as LO1/2. Given the lateral/medial distinction of FEF in the macaque, is it possible that TMS stimulation of FEF targeted a more foveal region than the seed region for the BOLD correlations?The authors should address sources of variability in the localization of areas. Closer evaluation of the correlation maps could potentially address this. While the pre- and post- TMS correlation maps of FEF heavily overlap in the ipsilateral hemisphere , there iThe model predicts that TMS to V1/V2 leads to a general increase in connectivity across cortex, and TMS to FEF leads to a general decrease. Can the model speak to potential topographically specific interactions as noted above?What accounts for the differences between the model predictions  and the Reviewing Editor:I was puzzled by the data in I agree with Reviewer #3 that the folded surfaces make it hard to see some sites (esp. FEF) and this could be resolved with partial cortical inflation or other views .eLife. Your revised article has been evaluated by Eve Marder (Senior Editor), Jody Culham (Reviewing Editor), and 3 reviewers .Thank you for resubmitting your work entitled "A hierarchy of timescales explains distinct effects of local inhibition of primary visual cortex and frontal eye fields" for further consideration at eLife, we try to avoid putting authors through an endless gauntlet of unnecessary revisions, we also aim to ensure that published manuscripts meet our high standards. In this case, the remaining concerns were substantive enough that we think another round of revisions would greatly benefit the clarity and impact of the manuscript.The manuscript has been improved considerably and two of the reviewers are largely satisfied. However, there are some remaining issues that still need to be addressed, as outlined below. Although at Again, since the detailed points made by the reviewers were well articulated and are appended in full after the summary from the Reviewing Editor.Essential points1) The manuscript has become clearer by rephrasing in terms of changes to positive and negative correlations . However, both Reviewer #3 and the Reviewing Editor still found it hard to interpret without an additional figure to show the post-TMS connectivity. (not just pre and post-pre changes). See their comments for details .2) Although based on your reply, we understand your justification for displaying data on the folded cortical surfaces, the problem remains that one of your two key areas (FEF) is hardly visible from lateral views. This can be solved simply by presenting either a superior view or a horizontal slice consistently in all figures  In the post-review consultation, the other reviewers reinforced a suggestion from Reviewer #3 . Reviewer #1 stated: I think the one issue to resolve is whether the model does or does not predict anti-correlations, that is, the authors should clarify the connection between their description in the main text and the results shown by Recommended considerationsReviewer #3 raised two points that the other reviewers (in post-review consultation) were less convinced were essential to resolve. Nevertheless, the Reviewing Editor would encourage you to take this feedback into consideration in case you can use it to strengthen the manuscript.1) Reviewer #3 (Point 2) still questions the asymmetry of the FEF and V1/V2 correlations,. The Reviewing Editor thinks that if it cannot be fully resolved, it should at least be made more apparent in the main manuscript (by showing the FEF clearly in extant figures and/or moving 2) Further consideration of the earlier results of Ruff and colleagues may be warranted . The other two reviewers were less convinced of this and noted that the TMS methods by Ruff were quite different and your paper wasn't designed to examine the relationship between connectivity and retinotopy. Nevertheless, Reviewer #3 thought the manuscript could be more compelling on this front . The Reviewing Editor will leave it as "your call" as to whether you can use Reviewer #3's suggestions to strengthen your manuscript or agree more with the other two reviewers that it is beyond the scope of your aims.Other points should be considered at the authors' discretion.Reviewer #1:I have gone through the authors' response and am happy with the additional analyses and revisions. My only (and slight) reservation is that the introductory sentence  to the section on "Anatomical connectivity" continues to be a little too optimistic, given that there is an ongoing debate about the pitfalls of tractography . Otherwise, I do not see any major remaining problems and would recommend acceptance of the paper.Reviewer #2:The authors have provided a thorough and thoughtful set of answers to my comments and I have no further concerns.Reviewer #3:The authors made extensive revisions to the manuscript including several new figures and provided a thorough and thoughtful response to the previous reviews. While the revision addressed several issues, my main concern regarding the interpretation of the correlation changes and their relation to the TMS targets and seed regions remain.1) The relationship between positive / negative correlations and increased / decreased correlations needs further clarification:Seventh paragraph of Results section. "Inhibitory TMS of right visual cortex (V1/V2) resulted in the emergence of positive correlations between this region and bilateral FEF." – The difference maps in Results section, subsection “Computational modeling”. "Simulation results showed that virtual inhibition of right V1/V2 within the model increased the positive correlations between this region and the rest of the brain red in ," – I'm Same section. "Conversely, simulated inhibition of FEF, again by slowing its intrinsic frequency, was associated with the emergence of significant anticorrelations in this region's connectivity with the rest of the brain blue in ." – In t2) The authors refer to a common V1/V2 region  across TMS targets, seed ROI and observed correlations. However, asymmetry in the correlations and anatomical variability suggest that this is not necessarily the case. The relationship between all three for V1/V2 and FEF needs further clarification.The data would be more compelling if the seeds were better matched to the observed correlations. For example, the FEF seed could be adjusted to be in better correspondence with the V1/V2 anticorrelations (or vice versa). Further, why not use the FEF seed as an ROI to evaluate correlations pre- and post- V1 TMS (and vice versa)? An ROI approach would be a more direct way for assessing correlation changes due to TMS .The FEF seed is noted as being posterior and lateral to the V1/V2 baseline anticorrelation. From the 3) As stated previously, Ruff and colleagues  found decreased correlations with foveal V1 and increased correlations with peripheral V1 after TMS to FEF. While their experimental paradigm differed with the current study and the reviewers' comment on state dependent effects is well taken, these prior studies looked at correlations during both task  and rest  conditions (and found no significant differences). Such findings are clearly relevant to the current work and should be discussed. How can these prior data  be reconciled with the current proposal on how TMS differentially affects the timescales of early visual cortex and frontal cortex?Added Comments from Reviewer #3 during Post-review discussion:The current study proposes a general difference between early visual and higher order regions with interactions governed by their intrinsic timescales of processing. The Ruff studies found that interactions between early visual cortex and FEF vary depending on the topographic sub-region within early visual cortex. Aside from methodological differences, those data suggest heterogeneity in early visual-frontal interactions . It's unclear to me how any such heterogeneity can be directly accounted for by the current study's broad short and long timescale differences in early visual cortex and FEF, respectively. More so, it actually seems that their model predicts the opposite (lack of heterogeneity). I think an argument potentially could be made that any heterogeneity in correlations reflects the heterogeneity in connections, though it's not clear to me what that mapping would be, and their data do currently do not speak to this.On a related note, higher precision in the targeting could mitigate this issue. I completely agree with [one reviewer] that there is a limit to what the authors can do in regards to the localization. However, they easily could have performed seed-to-seed correlations or used an areal atlas (particularly for V1), which would be much more of a control in localization than what they currently have done. These are very simple analyses, and frankly, I don't understand why these weren't done in the first place.I completely agree with [another reviewer] that it would not be fair to expect the authors to directly address this in their paper as their model clearly wasn't intended to test retinotopy or any other areal substructure. However, I think it's reasonable to expect the authors to have an idea (discuss) on how their model would fit within the well established architecture of the brain regions they've specifically investigated, particularly since their imaging results look like they do not encompass the whole area of V1 or FEF.For clarification, I was referring to the eccentricity specific correlations for visual areas V1-V4 from TMSing FEF in the Ruff 2009 paper, which seems to expand upon the V1 result in their 2006 paper.Reviewing Editor:In trying to wrap my head around the data given the new presentation in terms of not just the direction of changes but their effect on positive and negative correlations, I had to make visual comparisons of  Summary of reviews:While all reviewers of the work were quite positive, they raised a number of concerns that should be addressed in a revision. Although the journal policy is usually only to provide a summary of the main points, given that the reviewers raised a number of important and thoughtful points that may be "lost in translation" by condensation, the full reviews are appended below. The Essential Changes are based on the post-review discussion amongst the reviewers and editor and are outlined below. We also strongly recommend the authors review the full list of suggestions from the reviewers and, at their discretion, determine whether they can improve the manuscript based on the constructive criticism provided. The editor did not think that suggested changes requiring collection of new data were essential, though there are some cases where additional analyses of the extant data could be beneficial in addressing the suggestions.Essential changes:1) Most imperatively, two reviewers noted that there is some confusion between the concepts of anticorrelation and connectivity changes that is challenging for the interpretation of the results.Reviewer #3 states, "Positive and negative correlations are not synonymous with increased and decreased connectivity. The authors summarize their findings as "stimulation of early visual cortex selectively increased feedforward interactions with FEF" and " stimulation of FEF decreased feedback interactions with early visual areas." TMS of FEF resulted in a stronger anti-correlations with V1/V2. An argument could easily be made that connectivity increased in both cases, especially since these areas appear to be moderately anti-correlated at rest (see below)."Similarly Reviewer #2 points out that the interpretation of connectivity changes depends not solely on changes to correlation values but the magnitude of the r value with respect to no correlation (r=0).This must be clarified with more careful wording, unpacking of the specific effects  and bettWe thank the reviewers for highlighting our lack of clarity on this important definition. We agree that the sign of the correlations – positive versus negative – is not synonymous with increased and decreased connectivity, respectively. To avoid any confusion we now present the results as TMS-induced changes in positive or negative correlations  relative to baseline.“Effects of local inhibitory TMS on functional connectivity”:We have now clarified the text in the section titled The following sentences in the Discussion have also been clarified:Second paragraph:“A novel finding of our study is that inhibition of early visual cortex resulted in the emergence of positive correlations between this region and FEF, along with other striate and extrastriate areas.”“Our finding that unilateral inhibition of early visual cortex increases the positive coupling with FEF adds to a growing literature.”rd paragraph:Beginning of the 3“In contrast to the increased positive coupling apparent …”The Results section, subsection “Computational modelling” has also been amended:“Results from functional and effective connectivity analyses suggest that TMS of V1/V2 stimulation  increase“Simulation results showed that virtual inhibition of right V1/V2 within the model increased the positive correlations between this region and the rest of the brain red in , …”“Conversely, simulated inhibition of FEF, again by slowing its intrinsic frequency, was associated with the emergence of significant anticorrelations in this region’s connectivity with the rest of the brain blue in .”Post TMS > Baseline  instead of the ambiguous terms of “increased/decreased connectivity”. Similarly we changed the text in The figure legends for 2) Two reviewers (#2 and #3) raised questions about the localization of the TMS sites. This must be clarified.The localization of stimulation sites was initially based upon MNI coordinates in standard space defining the centroid of the targeted brain regions . These coordinates were then manually refined using established anatomical landmarks at the level of individual anatomy . As requested, we now provide a new figure  to clari3) Reviewer #1 raised concerns about the accuracy of the structural connectome on which the Kuramoto model is based. The other two reviewers agreed this was a concern so this must be discussed.http://www.mrtrix.org/). However, to test the impact of possible biases in the connectome reconstruction on our computational findings we have now run two further analyses.The estimation of structural connectivity was performed on data from a large sample of healthy adult participants (N = 75) using state-of-the art probabilistic tractography algorithms. These algorithms have good sensitivity . Dense structural matrices maximize the sensitivity  but have lower specificity . Conversely, sparser matrices maximize specificity but lack in sensitivity. Running the model using different matrix thresholds changes the balance between sensitivity and specificity. Results from this analysis replicated the main results of the model .distance correction replicated those originally presented. Specifically, the variation in functional connectivity between the original matrix , the 30% matrix  and the matrix penalizing long-range connection  were all consistent with the original findings.Second, we re-ran the model using a connectivity matrix that incorporated a correction for the known tractography bias in reconstructing short- versus long-distance connections. Because of the increased likelihood of randomly seeding larger long-range fibers, we have penalized the long-range connections by dividing the weights by the fiber distance . Model results obtained using this To address this legitimate concern, the following sentences have been added in the section :“Probabilistic tractography combined with the relatively large sample size allowed an accurate estimation of the average connectivity strength of pairwise regional connections in the adult population. However, to ensure that the findings of our model were robust to possible inaccuracies in streamline reconstruction, we ran the model using (i) a sparser structural connectivity matrix , and (ii) a correction for the reconstruction of long-range connections . These analyses replicated the original result showing that TMS of FEF increases the discrepancy between activity in this brain region and V1/V2 whereas TMS of V1/V2 decreases the discrepancy. This robustness is largely explained by the fact that FEF consistently has a high degree  whereas V1/V2 is consistently a peripheral node with a comparatively low degree.”4) The Reviewing Editor raised a concern about the asymmetry in connectivity between FEF and V1/V2 at baseline and the other reviewers agreed this should be addressed. Specifically, the data in The Reviewing Editor is correct in expecting that the correlation between two regions should be roughly symmetric . The lack of a precise matching between the two is likely due to two factors. First, we performed a seed-to-voxel functional connectivity analysis and not a seed to seed  analysis. This means that the clusters of voxels in FEF  and early visual cortex  were similar but not identical. The absence of a significant anticorrelation between the FEF seed and early visual cortex at baseline may therefore be due to the fact that the FEF seed (red in the figure below) is located slightly laterally and posteriorly relative to voxels showing the highest anticorrelations with the V1/V2 seed  Two reviewers agreed the manuscript would be strengthened by the inclusion of control sites in the connectivity analysis while a third reviewer disagreed. The most useful additional analysis that the majority agreed would be worth investigating would be to examine the connectivity of the V1/V2 site after FEF stimulation and vice versa. We suggest the authors check this out and see if it adds any value to the paper (and include it if it does or provide a brief summary in the reply letter if it doesn't).The transitivity of the correlation coefficient as a measure of connectivity  limits nodes which we can use as potential nulls. Because functional connectivity with V1 changes upon TMS of FEF, the Pearson’s correlation will change between V1 and all other regions showing a change of functional connectivity with FEF. Therefore V1 cannot be used as a null region. To address the reviewers’ comment with this caveat in mind, we conducted two new sets of analyses:First, we assessed changes in functional connectivity pre- and post-TMS (for both V1/V2 and FEF sessions) using a control region ipsilateral to the TMS stimulation site but outside the networks of interest . These analyses revealed no significant changes in functional connectivity (p< 0.05 FWE corrected at cluster level) between the pre- and post-TMS scans (for both V1/V2 and FEF sessions). The following sentences have been added at the end of p. 7:“We also assessed changes in functional connectivity pre- and post-TMS (for both V1/V2 and FEF sessions) using a control region ipsilateral to the TMS stimulation site but outside the networks of interest . These analyses revealed no significant changes in functional connectivity between the pre- and post-TMS scans, for both V1/V2 and FEF sessions.”simulation analyses. These analyses were undertaken to exploit the potential of the model to undertake “virtual” TMS and hence examine whether our functional connectivity results could arise from ‘non-specific’ effects of local stimulation. To this end, we examined whether:Second, we undertook two new (i) Simulated inhibition of the right postcentral gyrus  and left superior temporal gyrus  would result in changes in functional connectivity similar to those observed following simulated TMS of V1/V2 and FEF;(ii) Simulated inhibition of the right postcentral gyrus and left superior temporal gyrus could result in specific changes in functional connectivity between early visual cortices and FEF.These simulations demonstrated that inhibition of either of these two control regions was associated with markedly different changes in functional connectivity compared with those observed following inhibition of V1/V2 and FEF  of cortical regions not relevant for our hypotheses – in this instance areas in the vicinity of the right postcentral gyrus and left superior temporal gyrus – could also produce effects on functional connectivity similar to those observed after simulated inhibition of V1/V2 or FEF. Second, we tested whether inhibition of these two control regions would cause specific changes in functional connectivity between early visual cortex and FEF. Simulated inhibition of either control region yielded a markedly different change in connectivity than that observed following simulated inhibition of V1/V2 or FEF see . MoreoveReviewer #1:This paper presents a combination of TMS with different analyses of functional/effective connectivity and computational modelling, using a whole-brain formulation based on the Kuramato model. This combination is so far unique and enables the authors to provide evidence for a plausible and quite fundamental principle of cortical organisation, i.e., that cortical areas at different hierarchical levels operate at different time scales. I think this is a strong and innovative paper which elegantly combines complementary techniques. However, from my perspective, a few issues would benefit from clarification or reformulation:1) I think the presentation of the key findings and the conclusions drawn needs to be improved. In brief, the key conclusion seems to be: inhibitory TMS leads to a local slowing of the timescale (frequency of dominant oscillations) at which an area operates; TMS of V1/V2 decreases and TMS of FEF increases the discrepancy in oscillatory frequencies between these two levels of the visual hierarchy; this explains why the former leads to increased bottom-up connectivity and the latter leads to reduced top-down connectivity. The authors get to this on fourth paragraph in the Discussion, but do not make it as explicit as I think they should.In response to the reviewer’s comment the following sentences have been added:Final paragraph of Introduction:“Experimental and modelling results support the notion that local inhibition of early visual cortex reduces the discrepancy in endogenous synchronization between lower and higher levels of the visual cortical hierarchy, whereas inhibition of FEF increases the discrepancy. Our work therefore provides novel insights into the neural mechanisms that underlie the effects of local inhibition on large-scale brain dynamics.”Fourth paragraph of Discussion:“The observed changes in connectivity following inhibitory TMS may therefore be explained in terms of a reduction in synchronization discrepancy following local stimulation of V1/V2 and an increase in synchronization discrepancy following stimulation of FEF.”Moreover, it would help if this interpretation and the underlying hypothesis would be pointed out more clearly earlier in the paper, e.g. in the Introduction.In keeping with our previous response, two sentences have been added at the end of the Introduction. This change also complies with the journal style of outlining the main results, and significance, at the end of the Introduction.2) To support this conclusion, it would be helpful if not only the correlation between changes in ALFF and changes in connectivity were shown; additionally, it would be important to see the magnitude and direction of the local changes in ALFF, separately at both levels of the hierarchy, that are induced by TMS.minus post-TMS) – see new We now provide a table of the individual ALFF values for each participant as a function of targeted region  and condition  One potential concern for the whole-brain Kuramato model is the accuracy of the structural connectome on which the model rests. This connectome was derived from diffusion-weighted imaging data, using probabilistic tractography. It would be helpful if the authors could provide some reassurance that these connectivity estimates are robust and not overly affected by methodological problems of tractography, such as the known bias in reconstructing short- versus long-distance connections.rd main comment  for a comprehensive treatment of this issue.Please see our response to the 34) Testing the robustness of the functional connectivity results across a variety of preprocessing strategies for resting state data is a strong aspect of this paper. However, it did not entirely get clear what motion correction procedure the results eventually reported are based on. It would be helpful if this could be clarified.We have now clarified this in the Materials and methods (Eighth paragraph):“Thus, the results reported reflect the outcomes following motion correction .”Reviewer #2:The aim of the study was to explore long-range connectivity changes (assessed by resting-state fMRI) resulting from a local decrease in excitability induced by continuous theta burst TMS (cTBS). The authors used cTBS to modulate excitability in the V1 and the R-FEF in separate sessions. Inhibition of V1 with cTBS led to an increase in functional connectivity between V1 and R-FEF. Inhibition of R-FEF led to a decrease in functional connectivity between R-FEF and V1. This is an interesting study that looks to combine brain stimulation and neuroimaging with modelling to address a clearly articulated question.Major Comments:1) The authors suggest that there is a significant relationship between the amplitude of local BOLD signal within the FEF and changes in connectivity between FEF and V1 . Could tThis is a difficult problem to address, and I admit I do not have an easy solution, but it should be at the least discussed. I admit that I do not understand the sentence "the effects remained after controlling for changes in mean BOLD signal between baseline and post-TMS sessions", which may be aimed at addressing this conflict.anticorrelations following stimulation of the FEF. Second, the fact that the correlations remained comparable after controlling for changes in mean BOLD signal across sessions further limits the likelihood that such correlations were simply driven by general fluctuations in SNR. Finally, we also examined activity within homologous visual areas in the opposite (non-stimulated) hemisphere. There were no significant correlations between changes in functional connectivity and changes in the amplitudes of slow signal fluctuations  extracted from homologous visual areas in the left hemisphere.While we understand the reviewer’s concern, here we argue that such an explanation is unlikely to account for the specificity and directionality of our results. First, we have shown that local changes in ALFF – following an identical stimulation protocol – are correlated with changes in functional connectivity and that these changes occur in opposite directions for the two TMS targets. That is, while a TMS-induced reduction in local ALFF correlated with the emergence of positive correlations with FEF following V1/V2 stimulation, a TMS-induced reduction in local ALFF correlated with the appearance of significant 2) The authors present no controls for their study. There are a number of questions raised by this:a) Can they demonstrate that the fluctuations in connectivity between V1 and FEF, separated in time as these scans were, cannot explain the changes here ? It may be possible to get this data from existing datasets – a step towards this would be to study the differences between the two baseline sessions, though this would not account for changes due to differing time spent in the scanner.b) Are these changes specific to the stimulated regions? If the seeds for the functional connectivity analyses are placed elsewhere in the functional networks, can similar changes be elicited? And similarly for regions outside the networks? The authors have the data to perform these analyses.c) Are the changes seen specific to stimulation at these sites? If the network was stimulated at a different site, would this result in the same pattern of results (i.e. is this just a reflection of some perturbation to the network as a whole or does it reflect the specific connectivity between these regions). This would require more data to be acquired, but is important for their conclusion that these are specific effects. A step towards this might be to show that stimulation of the FEF does not lead to connectivity changes in the V1 seed and vice versa, which they have the data to demonstrate.“Recommended considerations”. Our extensive new control analyses, on real and simulated data, directly address the reviewer’s concerns.Please refer to our response to the above 3) One of the major things I struggled with in this study is the authors' interpretation of ante-correlations. To me, the demonstration of a significant, negative functional connection between two regions is not a lack of a functional relationship, a point with which the authors seem to agree. Therefore, a numerical increase in the r value between two regions is not an increase in connectivity if it does not go through 0, but rather is a decrease in (inhibitory) connectivity. If it does go through 0 it is then a reversal of negative to positive functional connectivity and the interpretation is very different.This is an important point of interpretation, and is currently not clear in the manuscript. If I understand correctly, the authors suggest that inhibitory TMS to V1 led to an increase in connectivity between V1 and FEF. These regions were negatively correlated at baseline . Does thLikewise, inhibitory TMS to the right FEF led to a significant decrease in functional connectivity between this region and V1. Does this mean that there is now a significant functional connectivity between these regions, where there was not previously?Essential changes”.Several sections of the manuscript have been revised accordingly; please see our response to the first comment in the section “Reviewer #3:The authors combine fMRI, TMS, and computational modeling to investigate interactions between visual cortical regions. They find that cortical stimulation of posterior visual cortex (V1/V2) leads to an increase in BOLD correlations with FEF, but stimulation of FEF leads to a decrease in BOLD correlations with posterior cortex. The modeling work indicates that these changes are related to the intrinsic timescales of these regions. This is a truly impressive study. The multi-methodology approach and research question is very novel and the results have important implications on the neural processes facilitating interactions along the cortical hierarchy. The manuscript is well written. Several aspects of the data that could affect the interpretation of the results need clarification. Specifically, the authors should evaluate potential influences of the underlying functional organization on the observed interactions, and address the differences between the imaging and modeling results. In addition, I have concerns on the specificity of the localization for both the imaging data and TMS. Conceptually, the authors also need to better address the relation of positive/negative BOLD correlations to increased and decreased connectivity.Main Comments:Positive and negative correlations are not synonymous with increased and decreased connectivity. The authors summarize their findings as "stimulation of early visual cortex selectively increased feedforward interactions with FEF" and " stimulation of FEF decreased feedback interactions with early visual areas." TMS of FEF resulted in a stronger anti-correlations with V1/V2. An argument could easily be made that connectivity increased in both cases, especially since these areas appear to be moderately anti-correlated at rest (see below).Essential changes” section.We agree with the Reviewer’s comment and have changed the text accordingly. Please refer to our response to the first comment in the “Are the observed interactions between V1/V2 and FEF generalizable to posterior visual vs. higher-order  regions? The modeling certainly appears to predict generalizable effects, but I'm not sure the imaging data specifically demonstrate this.In the original analysis  broader Could the correlation patterns be related to the underlying topographic organization? Anatomical and functional studies in monkeys have shown that FEF and visual cortex is topographically organized with foveal and peripheral V1/V2 connected to differentiable portions of FEF . The particular increase/decrease in BOLD co-fluctuations may reflect where stimulation was applied relative to the functional organization of these regions . This would still be an interesting result, but should be clarified.The proximity of the FEF stimulation sites and the relatively sparse effect of TMS precludes any unequivocal assessment of distinct changes between FEF sub-regions and foveal/peripheral cortical areas. We did, however, endeavor to address the reviewer’s query by testing for possible associations between the anatomical location of the stimulated sites and the broad changes in local neural activity (ALFF) and functional connectivity following TMS , were increases in BOLD correlations observed in peripheral V1/V2 for Post-FEF-TMS?We did not find this to be the case in our data . This result could be due to the fact that we assessed resting-state dynamics and not task-based dynamics .Were any decreases in correlations observed in frontal cortex for POST-V1-TMS? If so, would this be predicted by the model?nd paragraph p.7):No. We have edited the revised manuscript to state . This panel shows the position and extent of cytoarchitectonically defined visual areas in the right hemisphere of ten human brains, B= TMS sites used in our study).Given the lateral/medial distinction of FEF in the macaque, is it possible that TMS stimulation of FEF targeted a more foveal region than the seed region for the BOLD correlations?Our results do show a relatively widespread effect of FEF stimulation on V1, V2 and other extrastriate visual areas . This laThe authors should address sources of variability in the localization of areas. Closer evaluation of the correlation maps could potentially address this. While the pre- and post- TMS correlation maps of FEF heavily overlap in the ipsilateral hemisphere , there is some separation with the post-TMS increase located slightly more inferior and lateral. Slices end at z = 40 in this figure though. Does the increase in correlation post TMS extend further inferior? What is the relationship between the observed correlations in FEF and the seed FEF?To follow up on the reviewer’s comment we investigated TMS-induced changes in FEF. The right cluster extends slightly inferiorly (up to Z = 36) but does not extend further backward. The following sentence has been added in the legend for “Note that the FEF cluster showing positive correlations with V1/V2 following V1/V2 stimulation extends slightly inferiorly (up to Z = 36).”The model predicts that TMS to V1/V2 leads to a general increase in connectivity across cortex, and TMS to FEF leads to a general decrease. Can the model speak to potential topographically specific interactions as noted above?Preliminary investigations suggest that there are a host of topographical specificities and nuances that accompany virtual simulations. However, undertaking such work, and extracting underlying principles introduces substantial computational complexities that mandate a complete study of its own. To highlight this we have added the following sentence in the Discussion (Fourth paragraph of Discussion):“Within this broad context, preliminary analyses suggest that there are a host of topographical specificities and nuances that accompany virtual simulations. Further work is needed to address the specificity of our model and the source of possible discrepancies between virtual and empirical findings.”What accounts for the differences between the model predictions  and the Several factors might explain the discrepancies between the threshold results of the modelling and the experiment. In general, the model represents a considerable simplification of the underlying complex neuronal processes. Thus, the correspondence between the computational model and the empirical findings is necessarily limited. For example, empirical data are limited and thus underlying effects may not reach statistical thresholds. There are likely also considerable regional heterogeneities and related complexities that are not incorporated in the model. We have discussed the need to address possible discrepancies between model and data with the aforementioned edits to the Discussion (see above).Reviewing Editor:I was puzzled by the data in Essential changes”.Please refer to our response to the fourth comment under “Essential points1) The manuscript has become clearer by rephrasing in terms of changes to positive and negative correlations . However, both Reviewer #3 and the Reviewing Editor still found it hard to interpret without an additional figure to show the post-TMS connectivity. (not just pre and post-pre changes). See their comments for details .To address this issue, we extracted single-subject average baseline values of functional connectivity in key clusters showing a significant TMS effect. We then calculated group-level mean connectivity, for both baseline and post-TMS clusters. These results are now presented in a new Table ,. They aAccordingly, we have modified the manuscript as follows:Seventh paragraph of Results:“Inhibitory TMS of right visual cortex (V1/V2) resulted in the emergence of positive correlations between this region and bilateral FEF . TMS of Results section, subsection “Computational modeling”:“Conversely, reduced positive correlations following FEF stimulation were driven by a reduction in feedback connectivity .”Fourth paragraph of Discussion section:“In contrast to the increased positive coupling apparent after inhibition of early visual cortex, an identical inhibitory TMS protocol delivered over the right FEF led to a decoupling between the target region and bilateral early visual cortex.”Legend of “Legend of “Overlap of baseline and post-TMS connectivity between early visual cortex and FEF, bilaterally. Analysis revealed frontal regions that were anti-correlated with BOLD signal activity in the right occipital pole at baseline (illustrated in blue), regions that increased their connectivity  with right early visual cortex following inhibitory TMS (red-orange), and regions that were both anti-correlated with the right occipital pole and increased their functional connectivity with this region following TMS (pink). Note that the FEF cluster showing positive correlations with V1/V2 following V1/V2 stimulation extends slightly inferiorly (up to Z = 36). All p< 0.05 FWE, cluster level.”2) Although based on your reply, we understand your justification for displaying data on the folded cortical surfaces, the problem remains that one of your two key areas (FEF) is hardly visible from lateral views. This can be solved simply by presenting either a superior view or a horizontal slice consistently in all figures  In the post-review consultation, the other reviewers reinforced a suggestion from Reviewer #3 . Reviewer #1 stated: I think the one issue to resolve is whether the model does or does not predict anti-correlations, that is, the authors should clarify the connection between their description in the main text and the results shown by To address this comment we have added a new supplementary figure  showing Recommended considerationsReviewer #3 raised two points that the other reviewers (in post-review consultation) were less convinced were essential to resolve. Nevertheless, the Reviewing Editor would encourage you to take this feedback into consideration in case you can use it to strengthen the manuscript.1) Reviewer #3 (Point 2) still questions the asymmetry of the FEF and V1/V2 correlations. The Reviewing Editor thinks that if it cannot be fully resolved, it should at least be made more apparent in the main manuscript  above.


Bypass surgery is one of the most frequently used strategies to revascularize tissues downstream occlusive atherosclerotic lesions. For venous bypass surgery the great saphenous vein is the most commonly used vessel. Unfortunately, graft efficacy is low due to the development of vascular inflammation, intimal hyperplasia and accelerated atherosclerosis. Moreover, failure of grafts leads to significant adverse outcomes and even mortality. The last couple of decades not much has changed in the treatment of vein graft disease (VGD). However, insight is the cellular and molecular mechanisms of VGD has increased. In this review, we discuss the latest insights on VGD and the role of inflammation in this. We discuss vein graft pathophysiology including hemodynamic changes, the role of vessel wall constitutions and vascular remodeling. We show that profound systemic and local inflammatory responses, including inflammation of the perivascular fat, involve both the innate and adaptive immune system. Occlusive atherosclerotic disease is a leading cause of mortality and morbidity worldwide. The most commonly used revascularization strategies to unblock or circumvent atherosclerotic lesions are balloon angioplasty (with or without stenting), endarterectomy and bypass surgery. For patients with left main coronary artery disease (CAD), three-vessel CAD and patients with late-stage peripheral artery occlusive disease (PAOD) bypass surgery is the primary standard of care –4. For pIn this review, we emphasize the role of inflammatory processes during vein graft remodeling and show how inflammation is involved in all phases leading to VGD, Figure via these side branches when pressure is inflicted on the isolated venous segment, leading to a profound distension of the venous segment. In the next stage, directly after grafting the venous segment in the arterial circulation, the vein graft will be distended again due to the exposure to the arterial blood pressure. Both forms of distension lead to serious damage of the vessel wall  progenitor cells have been shown to contribute to re-endothelialization –40. InduDespite the fact that enhancement of re-endothelialization is beneficial in preventing VGD in preclinical studies, no effective therapeutic approaches exist to facilitate this process . TherefoProliferation and migration of SMCs are key elements in intimal hyperplasia formation. During harvesting and engraftment, SMCs within the vein graft are exposed to ischemia resulting in SMC apoptosis , 44, 45.Smooth muscle cells in vein grafts express different growth factors such as PDGF, TGF-β, vascular endothelial growth factor, and endothelin-1, which are major stimulators of intimal hyperplasia formation –54. TargVeins possess elastin fibers but lack defined external and internal elastic lamina. Therefore, fibroblasts when migrating from the adventitia to the intima in veins encounter little barriers. These adventitial fibroblasts are highly proliferative. Adventitial fibroblasts that acquire a smooth muscle-like phenotype are known to contribute to intimal hyperplasia formation , 65.+ progenitor cells  . The ECMPlasmin, formed by activation of plasminogen by plasminogen activators, can contribute to activation of MMPs and can cleave ECM components, such as laminin and fibronectin. These ECM degradation products subsequently can bind to pattern recognition receptors . Failed via binding to the TLRs activate the cells in the vessel wall resulting in the release of growth factors and cytokines. DAMPS can also activate platelets and thus enhance, due to the platelet expressed adhesion molecules P- and E-selectin, the attachment of circulating leukocytes and subsequent infiltration into the vessel wall  pathway, to activation of NF-κB and results in induction of cytokines (The late phase of vein graft development is characterized by oxidized low-density lipoprotein (ox-LDL) retention and subsequent lipid accumulation , 34. Phoytokines . In geneytokines , 113–115NF-κB is one of the most important transcription factors for promotion of inflammatory responses in vein graft remodeling . InhibitAs indicated above, TLRs play a crucial role in the early inflammatory triggers that initiate vein graft remodeling. Among the first mediators of inflammation in vein grafts are DAMPs such as aggrecan and heat shock proteins , 136. Enex vivo perfused human saphenous vein segments and reduced vein graft intimal hyperplasia in a murine model  and complement factors release granules containing tryptase, chymase, and histamine . Vein grAlso present in the perivascular region of vein grafts and especially in the adventitia are NK cells . Upon aclo monocytes, are found in all layers of the vein graft and colocalize with T cells as antigen presenting cells  3 and 7 results in a protective effect on vein graft remodeling. This is particularly of interest since IRF3 and IRF7 activation leads to expression of type1 interferons, that are subsequently involved in the activation of CD4 and CD8+ T cells  treatment have been shown to be effective in preventing graft occlusion , 167. InComparable to native atherosclerosis statins are included in the standard of care for patients undergoing vein graft surgery. The mode of actions of statins is primarily cholesterol lowering by inhibiting HMG-CoA reductase but other mechanisms are also described. Statins can improve endothelial function, prevent proliferation of SMCs and decrease activation of macrophages , 169. Stin vitro and experimental studies (A new therapy to prevent VGD is the use of an extravascular support. The extravascular support functions as a protective outer layer of the vein graft, thereby reducing wall tension, activation and stretching of SMCs and endothelial cells . Promisi studies –177. The studies . The VES studies , 180. FuThe use of vein grafts as a revascularization strategy is still necessary despite the unfavorable patency outcomes. Constrictive remodeling, intimal hyperplasia formation, and unstable atherosclerotic lesions are the main causes of VGD in both coronary and peripheral vein grafts. Histopathological studies of human vein grafts and experimental vein graft models have demonstrated that inflammatory components, especially those from the innate immune system, are crucial in all stages of vein graft development. Additional studies are required to prevent VGD and test new strategies for the treatment of vein grafts. Targeting inflammation either in a broad form or in a very specific has great potential as revascularization strategy for failing grafts.MV and PQ designed and wrote the manuscript.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 recent dramatic development of the Balkan Medical Journal , especiaThe Balkan Medical Journal had its beginning under the title of “Edirne Tıp Dergisi”  in May 1979 when Edirne Medical Faculty was a branch of İstanbul University in Cerrahpaşa Medical Faculty. In the summer of that year, the school gave its first graduates. During the presentation of the first issue, Professor Dr. Suat Vural, the owner of the journal and the dean of the medical faculty, launched a journal in the field of general medicine, which he hoped would be the scientific voice of Edirne Medical Faculty. The first two volumes were published in Istanbul. After movement from İstanbul to Edirne in 1982, Edirne Medical Faculty received a new name, “Trakya University School of Medicine.” Accordingly, “Edirne Tıp Dergisi” had its name changed to “Trakya Üniversitesi Tıp Fakültesi Dergisi” . Interestingly, the first two volumes were published by three different editors ; unlike today’s publishing conception, they were working more as an executive secretary than as an Editor-in-Chief.After publishing the second volume in 1980, the publication of the journal was interrupted for 5 years during the foundation period of Trakya University in Edirne. Beginning from the third volume in 1986, the journal resumed its publication life under the patronage of Associate Professor Çobanoğlu . He contIn 1996, Professor Karasalihoğlu was appointed as the Editor-in-Chief and served until 2002 . During In 2002, Professor Yalnız was appointed as a new Editor-in-Chief of the journal . Only a Professor Yalnız resigned in the first quarter of 2005, and Professor Ulugöl took over the task . ProfessIn 2005, with great efforts of Professor Ulugöl and the Associate Editors, the journal was covered by Ulrich’s Periodicals Directory, Oxford University Libraries, JournalSeek, and DOAJ. In 2006, the editorial team began to use the online submission and peer-review systems, which simplified and accelerated the publication process. These changes were important steps for the future of the Journal. Consequently, the number of submissions from outside of Trakya University began to increase steadily, reaching two-thirds of the total in 2007 . At the Three years after his leadership, some goals of Professor Ulugöl took place as it was mentioned in his first editorial. Certainly, the first important milestone for our journal was the inclusion into SCI-Expanded in 2008, which led to a significant increase in the number of submissions . This stAfter Professor Ulugöl’s resignation, Associate Professor Öztürk was appointed as the Editor-in-Chief .In fact, there is nothing wrong in calling his term as an era of several breaking changes. He immediately faced two challenges, i.e., increased number of submissions and financial problems. Beginning from January 2009, the editorial team decided to publish the journal quarterly and charge submission fee from authors.In 2010, Professor Öztürk was informed that the journal was accepted to be indexed in Scopus and Embase. This was a great news for us as Scopus is a citation database. In the same year, we started to get DOI number for journal articles. A few months after DOI’s application, we began using a plagiarism software to raise ethical standards , the International Council of Medical Journal Editors (ICMJE), the World Association of Medical Editors (WAME), the Council of Science Editors (CSE), and the European Association of Science Editors (EASE). With those memberships, the journal became committed to following their instructions .In 2013, we were informed that the journal was accepted to be indexed in PubMed/Central. This was very good news for the visibility and prestige of the journal. At the end of the same year, we decided to do something more for the journal’s prestige, and beginning from 2014, the submission fee was abandoned. These developments led to a dramatic increase in the number of submissions in 2015. Starting from 2016, the increase in the number of articles forced us to publish the journal bimonthly.Another characteristic of Professor Uzun’s period was the educational task of the journal. As read in Professor İnan’s editorial “In addition to independent, impartial and active sharing of scientific information with the whole world, the mission of the Balkan Medical Journal is to train the actors taking part in this process” . There aIn the fall of 2016, Professor Koçak handed over the editorial duty . For theProfessor Koçak always believed that institutions must have a memory. Sultan Bayezid II complex of Edirne, founded in 1488, is the first example of a centrally planned medical center and is considered to be the forerunner of modern hospitals . Figure One of the first things Professor Koçak did was to restructure the Editorial Board with the establishment of the Deputy Editors, the Editor-at-Large, and the Honorary Editor. Another innovation was the inclusion of brief reports and special section articles as new types of articles to be published. In the last quarter of 2017, Professor Koçak and Professor Karadağ (Web Editor) announced a new and improved website that will provide better service to readers, reviewers, and authors . UndoubtThe Balkan Medical Journal has now completed 38 years of publication and is well recognized by the international health community. By doing the right thing, especially applying high ethical standards, the Balkan Medical Journal now adheres to the internationally accepted criteria of modern medical journalism and has taken the well-deserved place in the Balkan region and in the world . Table 4


The increasing prevalence of coeliac disease (CD) and gluten-related disorders has led to increasing consumer demand for gluten-free products with quality characteristics similar to wheat bread. The replacement of gluten in cereal-based products remains a challenge for scientists, due to its unique role in network formation, which entraps air bubbles. When gluten is removed from a flour, starch is the main component left. Starch is used as gelling, thickening, adhesion, moisture-retention, stabilizing, film forming, texturizing and anti-staling ingredient. The extent of these properties varies depending on the starch source. The starches can additionally be modified increasing or decreasing certain properties of the starch, depending on the application. Starch plays an important role in the formulation of bakery products and has an even more important role in gluten-free products. In gluten-free products, starch is incorporated into the food formulation to improve baking characteristics such as the specific volume, colour and crumb structure and texture. This review covers a number of topics relating to starch; including; an overview of common and lesser researched starches; chemical composition; morphology; digestibility; functionality and methods of modification. The emphasis of this review is on starch and its properties with respect to the quality of gluten-free products. Patients diagnosed with coeliac disease (CD) make up 1% of the world population. However, the true number of people suffering from CD is expected to be higher . In bakeStarch is the primary source of stored energy in many plants including cereals, legumes, roots and tubers. It provides 70%–80% of the calories consumed by humans worldwide . In addiThis literature review covers the topics of production and extraction of the main starches, their functional and chemical properties, digestibility and modifications , with particular emphasis on the importance of such properties in the formulation of gluten-free products. Furthermore, a detailed table outlining the characteristics of common and less common starches is presented. www.aaf-eu.org) the main production areas of starch are North America (33%), China (33%), Europe (18%), South East Asia (11%) and South America (5%). North America, China and Europe make up 84% of the world production. The top 10 starch producing companies represent this geographic predominance. In 2010 these were Cargill (United States) with 8.0 million tonnes (MT); Ingredion  with 5.9 MT; ADM , 4.7 MT; Tate & Lyle , 3.6 MT; Roquette , 2.8 MT; Zhucheng Xingmao , 2.0 MT; Global Bio-Chem , 1.5 MT; Tereos Syral , 1.4 MT; COFCO , 1.3 MT and Xiwang , with 0.7 MT [Commercially available starches are obtained from various sources including wheat and corn , potato (tubers) and cassava (root). The worldwide production of starch is increasing. In three years 2009–2012) the starch production increased by 25% from approximately 60 million tonnes  to 75 mi09–2012 th 0.7 MT . The maih 0.7 MT .Over the past decades, a lot of research on starches in gluten-free formulations has been conducted. It is well known, that the characteristics of a starch  affect the formulation differently. However, the quality of gluten-free products is still considered poor . Table 1d-glucose  or confocal laser scanning microscopy (CLSM). The shape and size of starch granules depends on the origin of starch . Figure The digestion of starch is an important process with respect to dietary requirements . FactorsAs presented in Starch plays a major role in food products with a variety of applications . During Native starch is insoluble in cold water; in this state starch, does not have an effect on food characteristics. However, when heat is applied to starch in excess water, the starch undergoes changes, such as gelatinisation, pasting and retrogradation . Native starch granules have a complex architecture. Therefore, the processing of starch granules usually involves the disruption of the structural order within the granules during heating in water . This coWhen starch is heated in water, amylose leaches from the granules . The leaAdditional heating or shear at the stage of maximal swollen granules will destruct them by disrupting hydrogen bonding between polymer chains. When this happens, a dispersion of amylose and amylopectin and granule fragments is formed, which results in loss of viscosity of the paste . The poiRetrogradation is the slow re-association of solubilized starch polymers to a tightly packed structure, after heating in a fluid . RetrogrGelatinisation and pasting properties are key factors to determine the application of a starch in gluten-free breads. A simple gluten-free starch batter is a suspension of starch and yeast cells in water. The main aim during the baking process is to keep air and gas, introduced during mixing and fermentation, from escaping from the dough, and prevent starch and yeast from settling. Starch which gelatinises during the baking process increases the viscosity, which prevents settling of ungelatinised starch and the escape air and gas bubbles. The result is a set crumb structure . The quiThe formulation of gluten-free breads is a very important research area in food science. Various combinations of starch, flour, hydrocolloids, proteins and other minor ingredients have been investigated for the development of high quality gluten-free bread. Unmodified starches are used in many foods. They are used as thickener or gelling agents, for instance in gravies, custards, dressings, baked products and jelly gum candies. However, food products, made from native starches lack process ability for commercial manufacturing purposes . FurtherIn general, starch modification aims to produce a wide range of starches with different properties. The properties can contribute to a desirable appearance and texture, despite the above-mentioned requirements of processing and storage. Modification can make starches more process able by reducing the gelatinisation temperature or by reducing hot paste viscosity . ModifieStarches can be modified, using physical methods such as: pregelatinisation and dehydration . The litThe production of pregelatinised starches involves the cooking of starch, which leads to their pregelatinised state. When this state is reached, the starch is dried. Different methods of drying, such as drum drying, spray drying and extrusion cooking, are used.Drum drying is the commonly used method by industry . The metSpray drying of starch is considered to be the best method for the production of high quality pregelatinised starch . The maiExtrusion of starch is a crude procedure, which damages the starch granules more than drum drying. It is used to lower its molecular weight and increase the solubility of the starch. Thus, a reduction in viscosity and the fragmentation of amylopectin can be achieved . This leIn addition to the pregelatinisation of starch, several other approaches have been applied to modify starch without the use of chemicals or enzyme products. Heat treatments at different moisture levels affect starch properties, such as the granule morphology, crystallinity, swelling, gelatinisation and retrogradation. These treatments are classified as heat moisture, dry heat and annealing . The maiOver the last fifteen years new starch modifications, which can be applied in food systems have been introduced. One such modification produces superheated starch . The proModification by a multiple deep freezing and thawing method was investigated by Szymońska et al. . The maiHydrothermal treatment of corn starch by an instantaneous controlled pressure drop (DIC) was performed by the developers, Zarguili et al. . The aimThis review covered fundamentals of starch with the aim to better understand their influence in gluten-free systems. The initial problem is that the gluten-free products which have been produced from the main starch sources until now, still lack structure and taste, compared to products containing gluten. Even though modification of starch could help improve their performance, they need to be labelled on the product, which may lead some consumers to avoid such products. This review listed a variety of starches which are grown all over the world and are yet not include in gluten-free product formulations. These starches deliver a variety of compositional and morphological properties in their native state. Thus, they should be considered in fundamental research and development of gluten-free products. Furthermore, it is noteworthy, that many different extraction methods are used even for the same source of starch. Each manufacturer of starch has its own extraction progress or at least parameters which in turn leads to a different product and results in a different chemical composition of the produced starch. These differences in composition and potential differences in physical properties have a great impact on gluten-free products and their production. Hence, a certain consistency of batch to batch quality is needed to guarantee the production of gluten-free products of consistent quality.


T + tf/j (BTBR) mouse model of ASD and if the ketogenic diet, a diet previously shown to elicit therapeutic benefit in this mouse model, is capable of altering the profile.Gastrointestinal dysfunction and gut microbial composition disturbances have been widely reported in autism spectrum disorder (ASD). This study examines whether gut microbiome disturbances are present in the BTBRAkkermansia muciniphila content in the cecal and fecal matter of BTBR animals.Juvenile male C57BL/6 (B6) and BTBR mice were fed a standard chow  or ketogenic diet  for 10–14 days. Following diets, fecal and cecal samples were collected for analysis. Main findings are as follows: (1) gut microbiota compositions of cecal and fecal samples were altered in BTBR compared to control mice, indicating that this model may be of utility in understanding gut-brain interactions in ASD; (2) KD consumption caused an anti-microbial-like effect by significantly decreasing total host bacterial abundance in cecal and fecal matter; (3) specific to BTBR animals, the KD counteracted the common ASD phenotype of a low Firmicutes to Bacteroidetes ratio in both sample types; and (4) the KD reversed elevated Results indicate that consumption of a KD likely triggers reductions in total gut microbial counts and compositional remodeling in the BTBR mouse. These findings may explain, in part, the ability of a KD to mitigate some of the neurological symptoms associated with ASD in an animal model.The online version of this article (doi:10.1186/s13229-016-0099-3) contains supplementary material, which is available to authorized users. Autism spectrum disorder (ASD) encompasses several neurodevelopmental disorders in which seizures and gastrointestinal (GI) dysfunction exist as symptoms , 2. ResiT + tf/j (BTBR) mouse mimics the behavioral phenotype associated with ASD, although it lacks spontaneous seizure activity [The BTBRactivity , 7. The activity , has beeactivity . This woactivity , the impResearch investigating both brain and gut function has demonstrated that both tissues have various degrees of involvement in ASD progression , 9. A reT + tf/j mice  were age-matched to 5 weeks of age before being randomly selected for implementation of a standard chow  or ketogenic diet  for 10–14 days. The time point of 7 weeks of age for both B6 and BTBR mice was established to ensure both cohorts were post-pubertal in development [All experimental protocols were in compliance with the ethical standards approved by the University of Calgary Animal Care and Use Committee. Juvenile male C57BL/6 (B6) and BTBRelopment . Prior tPreviously published work on the BTBR mouse has examined the impact of the KD on several behavioral measures at similar ages as the mice in the present study . BrieflyFresh fecal samples were collected prior to sacrifice and cecal contents collected posthumously. All samples were stored at −80 °C until analysis. Using ~250 mg of fecal/cecal matter, total DNA was extracted and quantified as previously described . Microbip < 0.05 was considered to be significant. Microbial data was normalized and uploaded into MetaboAnalyst 3.0 for modeling using partial least squares discriminant analysis (PLS-DA). Variable importance of projection (VIP) scores were assessed in order to rank each microbial group for their degree of discrimination within the model.Statistical analysis was performed using IBM SPSS Statistics for Windows, version 20.0. Data are expressed as mean ± SEM. Differences between the genotype and diet were determined by analysis of variance (ANOVA), followed by Tukey’s post hoc test where The BTBR mouse exhibits many behavioral phenotypes relevant to ASD including impaired vocalizations and social interactions , 16. PreAkkermansia muciniphila content in the cecal and fecal matter of BTBR animals.The major findings of this study are as follows: (1) gut microbiota composition of cecal and fecal samples were altered in BTBR compared to control mice, indicating that this model may be of utility in understanding gut-brain interactions in ASD; (2) KD consumption caused an “anti-microbial”-like effect by significantly decreasing total host bacterial abundance in cecal and fecal matter; (3) specific to BTBR animals, the KD counteracted the common ASD phenotype of a low Firmicutes to Bacteroidetes ratio in both cecal and fecal matter; and (4) the KD reversed the elevated A. muciniphila, Methanobrevibacter spp., and Roseburia spp. in the cecal samples and A. muciniphila, Enterobacteriaceae, and Lactobacillus in the fecal samples.Our results identified distinct differences in animal mass between control and BTBR animals Table . FurtherBacteroides/Prevotella spp.) in fecal tissues [Cecal and fecal tissues were both assessed in the present study as research indicates that microbial abundance and diversity are directly related to the physiological role of each segment of the GI tract . Support tissues . HoweverClostridium cluster XI [Clostridium coccoides, Clostridium leptum, Clostridium clusters XI and I, Roseburia spp., and Lactobacillus spp.) [Examination of our data show several alterations in fecal gut microbiota of BTBR mice that are observed in patients with ASD, including elevated uster XI , decreasus spp.)  and incrus spp.) . Taken tResults of both cecal and fecal analysis showed a significant decline in total bacterial content upon implementation of the KD, in both animal genotypes Fig. . InteresC. coccoides and C. leptum [C. coccoides and C. leptum are saccharolytic bacteria that generate short-chain fatty acids (SCFAs) [C. coccoides and C. leptum populations, respectively, when cecal and fecal samples of BTBR-ketogenic animals are compared to all other groups  . Recent  (SCFAs) . Our datps Table . The abiA. muciniphila—the predominant representative of the Verrucomicrobia phylum within the GI tract [A. muciniphila. Our results indicate that BTBR animals fed a chow diet have significantly elevated A. muciniphila in both cecal and fecal matter , chow-fed animals. In fact, this bacterial species was the primary bacteria driving the calculated multivariate discrimination in both our cecal and fecal samples (Fig. A variety of neurological disorders, including ASD, is associated with various GI symptoms, including abdominal pain, inflammation, and impaired gastric motility . These sGI tract . ResponsGI tract , mucosalGI tract , and ASDGI tract , 2 are lles Fig. .In summary, this study demonstrates that KD consumption triggers gut microbiota remodeling in an animal model of ASD. These findings may provide insight into the therapeutic potential of KD manipulation by influencing gut microbial composition. Due to the paucity of research examining the role of the microbiome in KD therapy, we suggest further investigations into the complex interplay between the gut microbiome and the brain relevant to ASD and other neurological disorders.


Streptococcal bacteria are among dangerous human pathogens with major prevalence worldwide. A good vaccine against streptococcal bacteria should have epitopes that confer protection from infection by different streptococcal bacteria types. we aimed was to recognize the most immunogenic and conserved epitopes of streptococcal bacteria, which could be a potential candidate for vaccine development. Nineteen different M proteins of different streptococcal bacteria were chosen and analyzed. Nine-mer epitopes able to simulate both cells mediate and humoral immunity were predicted. Molecular docking was applied in order to measure free binding energy of selected epitopes. Final epitopes were analyzed if they were conserved among different streptococcal bacteria. The identified epitopes require experimental validation for their potential use in peptide vaccines. Streptococcal bacteria belong to the phylum Firmicutes and the order lactobacillus . StreptoIn establishing infection in the host, an initial and crucial step is the attachment of bacteria to the mucosa and dermal membranes . The maiTo reduce costs and experimental efforts in vaccine design, prediction of potentially immunogenic epitopes in a given protein would be a useful way . In the In this study, a reverse vaccinology approach was applied for epitope prediction of different M proteins of streptococcal bacteria. Epitope prediction is based on predicting epitopes that stimulate both humoral and cell-mediated immunity. Molecular docking was applied to model the interaction between the final epitopes with HLA-A1 allele. Conservation of peptides among studied M proteins was checked. Finally, selected epitopes were checked if they were promiscuous epitopes, which means they could be epitopes for several MHC class I and II alleles.Selection of sequences: The sequence of 19 different M proteins from different streptococcal bacteria, from NCBI and UniProt were selected. M proteins with their accession numbers are shown in T-cell epitope prediction: In the present investigation, MAPPP  [n [IC50] . EpitopeMolecular docking of the epitopes binding to HLA-A1: The 3D structure of selected epitopes binding to HLA-A1 was needed in order to do molecular docking. The 3D structure of selected epitopes was designed using PEPstr server, which predicts the tertiary structure of peptide from the sequence given as input , and ProPred was used to predict MHC class II epitopes [epitopes .Top 10 T-cell epitopes of each of 10 servers recognized by at least 10 servers were identified. The numbers of selected epitopes for each protein are shown in Conservation of the selected T-cell epitopes of each M protein among other M proteins was checked. 9 epitopes were conserved among studied M proteins, as listed in The introduction of genomic technologies such as recombinant DNA technology, in silico analysis, and proteomics revoluted the approach of studying bacterial pathogens and vaccine design. Genomic technologies say that every antigenic part of a pathogen can induce a protective immune response and should be tested to be used in vaccine design . One sucInitial and crucial step in colonization and establishing infection in the host is the adhesion of bacteria to the mucous and dermal membrane . SurfaceEarly studies in vaccine design against streptococcal bacteria were focused on using whole protein . Later sIn vitro and in vivo immunological tests should be performed in order to validate the suitability of the epitopes for vaccine development.In conclusion, in this work, antigenicity testing, B-cell and T-cell epitope prediction, and molecular docking has been done for different streptococcal M proteins. Our results show that nine different are appropriate candidates for peptide vaccine. Molecular docking results show that these nine peptides have low free binding energy in binding to MHC alleles, which means these peptides are good binders and can simulate the immune response.