Visual perception and imagery rely on similar representations in the visual cortex. During perception, visual activity is characterized by distinct processing stages, but the temporal dynamics underlying imagery remain unclear. Here, we investigated the dynamics of visual imagery in human participants using magnetoencephalography. Firstly, we show that, compared to perception, imagery decoding becomes significant later and representations at the start of imagery already overlap with later time points. This suggests that during imagery, the entire visual representation is activated at once or that there are large differences in the timing of imagery between trials. Secondly, we found consistent overlap between imagery and perceptual processing around 160 ms and from 300 ms after stimulus onset. This indicates that the N170 gets reactivated during imagery and that imagery does not rely on early perceptual representations. Together, these results provide important insights for our understanding of the neural mechanisms of visual imagery. If someone stops you in the street to ask for directions, you might find yourself picturing a particular crossing in your mind’s eye as you explain the route. This ability to mentally visualize things that we cannot see is known as visual imagery. Neuroscientists have shown that imagining an object activates some of the same brain regions as looking at that object. But do these regions also become active in the same order when we imagine rather than perceive?Our ability to see the world around us depends on light bouncing off objects and entering the eye, which converts it into electrical signals. These signals travel to an area at the back of the brain that processes basic visual features, such as lines and angles. The electrical activity then spreads forward through the brain toward other visual areas, which perform more complex processing. Within a few hundred milliseconds of light entering the eye, the brain generates a percept of the object in front of us.So, does the brain perform these same steps when we mentally visualize an object? Dijkstra et al. measured brain activity in healthy volunteers while they either imagined faces and houses, or looked at pictures of them. Electrical activity spread from visual areas at the back of the brain to visual areas nearer the front as the volunteers looked at the pictures. But this did not happen when the volunteers imagined the faces and houses. Contrary to perception, the different brain areas did not seem to become activated in any apparent order. Instead, the brain areas active during imagining were those that only became active during perception after 130 milliseconds. This is the time at which brain areas responsible for complex visual processing become active when we look at objects.These findings shed new light on how we see with our mind’s eye. They suggest that when we imagine an object, the brain activates the entire representation of that object at once rather than building it up in steps. Understanding how the brain forms a mental image in real time could help us develop new technologies, such as brain-computer interfaces. These devices aim to interpret patterns of brain activity and display the output on a computer. Such equipment could help people with paralysis to communicate. Visual imagery is the ability to generate visual experience in the absence of the associated sensory input. This ability plays an important role in various cognitive processes such as (working) memory, spatial navigation, mental rotation, and reasoning about future events . When weIn contrast, the temporal properties of perceptual processing are well studied. Perception is a highly dynamic process during which representations change rapidly over time before arriving at a stable percept. After signals from the retina reach the cortex, activation progresses up the visual hierarchy starting at primary, posterior visual areas and then spreading towards secondary, more anterior visual areas over time . First, However, the temporal dynamics of visual imagery remain unclear. During imagery, there is no bottom-up sensory input. Instead, visual areas are assumed to be activated by top-down connections from fronto-parietal areas . Given tIn the current study, we investigated these questions by tracking the neural representations of imagined and perceived stimuli over time. We combined magnetoencephalography (MEG) with multivariate decoding. First, we investigated the temporal dynamics within perception and imagery by exploring the stability and recurrence of activation patterns over time . Second,r = −0.45, p=0.02), which indicates that people with a higher imagery vividness as measured by the VVIQ also rated their imagery as more vivid on average during the experiment. Participants reported relatively high vividness on average . There was no significant difference in vividness ratings between faces (54.0 ± 29.7) and houses  = 1.46, p=0.16). To ensure that participants were imagining the correct images, on 7% of the trials participants had to indicate which of four exemplars they imagined. The imagined exemplar was correctly identified in 89.8% (±5.4%) of the catch trials, indicating that participants performed the task correctly. There was also no significant difference between the two stimulus categories in the percentage of correct catch trials  = −1.25, p=0.22). Furthermore, correctly identified catch trials were experienced as more vivid (52.06 ± 33.1) than incorrectly identified catch trials  = 3.11, p=0.0049), giving support to the criterion validity of the identification task. We had to drop one subject for this comparison because this person did not have any incorrect trials.Twenty-five participants executed a retro-cue task in which they perceived and imagined faces and houses and rated their experienced imagery vividness on each trial using a sliding bar see . Prior tTo uncover the temporal dynamics of category representations during perception and imagery, we decoded the category from the MEG signal over time. The results are shown in To reveal the generalization of representations over time, classifiers were trained on one time point and tested on all other time points  Figure . FurtherDuring perception, distinct processing stages can be distinguished , left. DIn contrast, during imagery, we did not observe any clear distinct processing stages. Instead, there was a broad off-diagonal generalization throughout the entire imagery period . AlreadyEven though we attempted to remove eye movements from our data as well as possible , it is theoretically possible that eye movements which systematically differed between the conditions caused part of the neural signal that was picked up by the decoding analyses . In ordeTo investigate when perceptual processing generalizes to imagery, we trained a classifier on one data segment and tested it on the other segment. We first trained a classifier during perception and then used this classifier to decode the neural signal during imagery . AlreadyFurthermore, the distinct processing stages found during perception , left weTo further pinpoint when perception started to overlap with imagery, we performed an additional analysis in which we reversed the generalization: we trained classifiers on different time points during imagery and used these to classify perception data. This analysis revealed a similar pattern of high overlap with perception around 160 and after 300 ms and low overlap before 100 ms and around 210 ms . Note thWe also investigated whether the temporal dynamics were influenced by imagery vividness by investigating whether the results of previous analyses were different for participants with high or low vividness , temporal shifting between trials would smear out this sequence over time. This would result in a broader diagonal pattern, where the width of the diagonal is proportional to the temporal shift between trials. This means that the broad temporal generalization that we observed during imagery could represent a sequential process if there were temporal shifts of more than a second on average between trials. Alternatively, the underlying process could be only sequential in the onset, such that different areas become active after each other, but remain active throughout time (or get reactivated later in time). In this case, temporal shifts between trials that are proportional to the difference in onset between the two processes would entirely obscure this dynamic. Note that this would mean that the start of significant imagery classification did not reflect the actual imagery onset, but the first point in time that the representations were consistent enough over trials to lead to above chance classification. Stimulus representations could actually be initiated before 350 ms after cue onset, but we would be unable to classify them at these early time points due to jitter in the onset. We cannot confidently rule out temporal uncertainty as an explanation for the broad temporal generalization at the onset of imagery. To fully resolve this issue, future research should systematically explore the effect of temporal uncertainty on different underlying processes and analysis tools need to be developed that can account for variation in temporal dynamics between trials.We observed clear overlap between imagery and perceptual processing around 160 ms after stimulus onset. The perceptual representation at this time likely reflects the face-specific N170. This component has been shown to be involved in face processing and appears between 130 to 170 ms after stimulus onset , which cThe lack of generalization between imagery and perceptual processing around 210 ms after stimulus onset was unexpected. This time window also showed an increase in temporal specificity during perception, indicating rapidly changing representations. One possible interpretation is that around this time feedback from higher areas arrives in low-level visual cortex . If low-Surprisingly, we did not observe any influences of experienced imagery vividness on the overlap between perception and imagery over time . One explanation for this is that we used whole-brain signals for decoding whereas the relationship between overlap and vividness has only been found for a specific set of brain regions . FurtherIn conclusion, our findings suggest that, in contrast to perception, at the onset of imagery the entire visual representation is activated at once. This might partly be caused by the nature of our task, since visual representations were already present in working memory at the onset of imagery. However, more research is needed to fully explore the contribution of temporal uncertainty between trials to this broad temporal generalization. Furthermore, imagery consistently overlapped with perceptual processing around 160 ms and from 300 ms onwards. This reveals the temporal counterpart of the neural overlap between imagery and perception. The overlap around 160 ms points towards a re-activation of the N170 during imagery, whereas the lack of overlap with perceptual processes before 130 ms indicates that either imagery does not rely on early perceptual representations, or that these representations are more transient and variable over time. Together, these findings reveal important new insights into the neural mechanisms of visual imagery and its relation to perception.We assumed a medium effect size (d = 0.6) which, to reach a power of 0.8, required twenty four participants. To take into account drop-out, thirty human volunteers with normal or corrected-to-normal vision gave written informed consent and participated in the study. Five participants were excluded: two because of movement in the scanner (movement exceeded 15 mm), two due to incorrect execution of the task  and one due to technical problems. 25 participants  remained for the final analysis. The study was approved by the local ethics committee and conducted according to the corresponding ethical guidelines (CMO Arnhem-Nijmegen).Prior to scanning, participants were asked to fill in the Vividness of Visual Imagery Questionnaire (VVIQ): a 16-item questionnaire in which participants indicate their imagery vividness for a number of scenarios on a 5-point scale . The VVIThe experimental task is depicted in http://www.tarrlab.org. Funding provided by NSF award 0339122). The house stimuli were adapted from the Pasedena houses database collected by Helle and Perona . We chose faces and houses because these two categories elicit very different neural responses throughout the visual hierarchy, during both perception and imagery ,  imagery , and areTo ensure that participants were imagining the stimuli with great visual detail, both categories contained eight exemplars, and on 7% of the trials the participants had to indicate which of four exemplars they imagined . For technical reasons, data from five sensors  were not recorded. Subjects were seated upright in a magnetically shielded room. Head position was measured using three coils: one in each ear and one on the nasion. Throughout the experiment head motion was monitored using a real-time head localizer . If neceSCR_004849). Per trial, three events were defined. The first event was defined as 200 ms prior to onset of the first image until 200 ms after the offset of the first image. The second event was defined similarly for the second image. Further analyses focused only on the first event, because the neural response to the second image is contaminated by the neural response to the first image. Finally, the third event was defined as 200 ms prior to the onset of the retro-cue until 500 ms after the offset of the imagery frame. As a baseline correction, for each event, the activity during 300 ms from the onset of the initial fixation of that trial was averaged per channel and subtracted from the corresponding signals.Data were analyzed with MATLAB version R2017a and FieldTrip  , 107 perception house trials (±12) and 105 imagery face trials (±16) and 106 imagery house trials (±13) remained for analysis. To remove eye movement and heart rate artifacts, independent components of the MEG data were calculated and correlated with the EOG and ECG signals. Components with high correlations were manually inspected before removal. The eye tracker data was cleaned separately by inspecting trials with high variance and removing them if they contained blinks or other excessive artifacts.To track the neural representations within perception and imagery, we decoded the stimulus category from the preprocessed MEG signals during the first stimulus and after the retro-cue for every time point. To improve the signal-to-noise ratio, prior to classification, the data were averaged over a window of 30 ms centered on the time point of interest. We used a linear discriminant analysis (LDA) classifier with the activity from the 270 MEG sensors as features  had a higher accuracy than a classifier trained at time t’ and tested at time t . This shows whether there is more information at time t than can be extracted by the decoder t’  and a low vividness  group. We produced the accuracy maps for all previous analyses separately for the two groups and compared the decoding accuracies of the two groups using cluster based permutation testing .It has been shown that representational overlap between imagery and perception, as measured by fMRI, is related to experienced imagery vividness . To inveDecoding accuracy was tested against chance using two-tailed cluster-based permutation testing with 1000 permutations . In the In order to identify the brain areas that were involved in making the dissociation between faces and houses during perception and imagery, we performed source reconstruction. In the case of LDA classifiers, the spatial pattern that underlies the classification reduces to the difference in magnetic fields between the two conditions see . TherefoSCR_001847). MNE-suite  was subsequently used to infer the subject-specific source locations from the surface reconstruction. The resulting head model and source locations were co-registered to the MEG sensors.For this purpose, T1-weighted structural MRI images were acquired using a Siemens 3T whole body scanner. Vitamin E markers in both ears indicated the locations of the head coils during the MEG measurements. The location of the fiducial at the nasion was estimated based on the anatomy of the ridge of the nose. The volume conduction model was created based on a single shell model of the inner surface of the skull. The source model was based on a reconstruction of the cortical surface created for each participant using FreeSurfer’s anatomical volumetric processing pipeline (RRID: The lead fields were rank reduced for each grid point by removing the sensitivity to the direction perpendicular to the surface of the volume conduction model. Source activity was obtained by estimating linearly constrained minimum variance (LCMV) spatial filters . The datTo facilitate interpretation and visualization, we reduced the two-dimensional dipole moments to a scalar value by taking the norm of the vector. This value reflects the degree to which a given source location contributes to activity measured at the sensor level. However, the norm is always a positive value and will therefore, due to noise, suffer from a positivity bias. To counter this bias, we employed a permutation procedure in order to estimate this bias. Specifically, in each permutation, the sign of half of the trials were flipped before averaging and projecting to source space. This way, we cancelled out the systematic stimulus-related part of the signal, leaving only the noise. Reducing this value by taking the norm thus provides an estimate of the positivity bias. This procedure was repeated 1000 times, resulting in a distribution of the noise. We took the mean of this distribution as providing the most likely estimate of the noise and subtracted this from the true, squared source signal. Furthermore, this estimate provides a direct estimate of the artificial amplification factor due to the depth bias. Hence, we also divided the data by the noise estimate to obtain a quantity that allowed visualization across cortical topography. For full details, see For each subject, the surface-based source points were divided into 74 atlas regions as extracted by FreeSurfer on the basis of the subject-specific anatomy . To enab In the interests of transparency, eLife includes the editorial decision letter and accompanying author responses. A lightly edited version of the letter sent to the authors after peer review is shown, indicating the most substantive concerns; minor comments are not usually included.eLife. Your article has been reviewed by three peer reviewers, and the evaluation has been overseen by a Reviewing Editor and Sabine Kastner as the Senior Editor. The following individual involved in review of your submission has agreed to reveal his identity: Nicholas Edward Myers (Reviewer #2).Thank you for submitting your article "Differential temporal dynamics during visual imagery and perception" for consideration by The reviewers have discussed the reviews with one another and the Reviewing Editor has drafted this decision to help you prepare a revised submission.Summary:In this paper by Dijkstra et al., entitled "Differential temporal dynamics during visual imagery and perception", the authors use machine learning methods (decoding) to compare the temporal dynamics of whole brain MEG signals during the perception of faces vs. houses on the one hand and the visual imagery of these same visual categories (faces vs. houses). Previous studies have described how much of the visual perception network is also recruited during visual imagery and how this recruitment depends on the specifics of the task design. The major novelty of this study is to address the temporal aspect of information flow in the relevant cortical networks. The methods are state of the art methods and the reported observations are straightforward.The reviewers and editors thought that greater attempts need to be made to make a point with respect to our understanding of imagery. At the moment, this paper is methodologically sound, carefully done, and well written. However, we think it has a very descriptive character, presenting a result that is not entirely surprising  and offering two different interpretations. That's a bit unsatisfying. To also make a stronger theoretical contribution, we think the authors should make a greater effort to disentangle these hypotheses .Essential revisions:1) How does the cross-temporal decoding of faces vs. houses compare to the cross-temporal decoding of faces (resp. houses) in each task. In other words, for each task, how does the cross-category signal variability compare to the within category signal variability in each condition. The reviewers and editors acknowledge that the authors go through a random permutation procedure to identify data-based chance level. Our question pertains to a better understanding of the neural bases of imagery: could it be that overall lower accuracies during imagery is due to a higher variability in within category information? This is a way to address whether the decay in decoding accuracy during imagery is due to weaker representations or increased representational variability.2) When presenting the temporal overlap between perception and imagery analysis, and Figure 3, the authors mention that the reported results describing that a classifier trained on perception can extract information from imagery data much earlier than a classifier trained on imagery data, might be due to the fact that more trials are available to the trained. This is a crucial point. We strongly recommend that the authors down sample their perception data to make this analysis  more comparable to the data presented in Figure 2. In this absence of this control, it is difficult to interpret the presented result.3) An important additional analysis that needs to be added is the analysis presented in Figure 2C on the high vividness and low vividness trials separately (on matched samples sizes). One expects there to have an overall lower decoding accuracy on low vividness trials. This analysis we expected to directly relate imagery decoding accuracy with the subjective report of imagery vividness. This would a be prior analysis to the one presented in Figure S2, addressing whether more overlap of imagery representation with perceptual representations correlated with higher vividness in imagery subjective experience (further discussed in the next point).4) The analysis presented in Figure S2, correlating perception/imagery representational overlap with imagery vividness is a crucial analysis to strengthen the scope of the present paper. It is based onto a LDA, thus assuming a linear relationship between imagery vividness and similarity between perceptual and imagery representations. One can expect other possible relationships between the two: e.g. a one or nothing mapping or a sigmoid type of mapping. We recommend to the authors to explore this differently. One way of doing it would be to perform to separate analysis as in figure 3 for high vividness imagery trials and low vividness imagery trials. One expects a difference to support the subject experience of imagery, though this difference might be localized to some cortical sources .5) The conclusion states that the 'findings show that imagery is characterized by a different temporal profile than perception.' This conclusion doesn't sound entirely surprising, and makes us think that the authors may be selling themselves short. We think it would be important to highlight what the results actually say about the neural basis of imagery.6) Furthermore, it might help the Discussion if the authors speculated how specific their results are to the task they chose, rather than imagery per se. In their task, imagery is always preceded by presentation of the exact stimulus to be imagined , which means that participants will still have a lot of information about the imagined stimulus in visual working memory. This would not be the case if, for example, participants were suddenly prompted to imagine a novel object. Could this partially account for the good cross-generalization between perception and imagery? What if there had been a control condition requiring memory but no imagery? Without such a control, how much of their findings may be attributed to visual working memory, rather than imagery?7) The clear decoding peak around 160 ms seems like it could be related to the N170 or M170 component. Since this component is so well-studied in terms of its role in face processing and its cortical sources in the ventral stream, it seems warranted to discuss this a bit more. Does the fact that the 160 ms peak cross-generalizes well across time and from perception to imagery indicate that face- or house-selective areas in the ventral stream are active at this time and then maintain activation later on, especially during imagery?8) The authors raise two different explanations for their results, e.g., in the Abstract: "These results indicate that during imagery either the complete representation is activated at once and does not include low-level visual areas, or the order in which visual features are activated is less fixed and more flexible than during perception." However, it is possible that there is a probably less exciting, yet more parsimonious explanation for the pattern of results.Perception and imagery could just evoke the same processing cascade , with two restrictions: (a) Imagery might not "start" as early as perception, thus not involving the earliest stages of perceptual processing. Thus, the onset of imagery decoding is expected to be later than the onset of perception decoding. (b) Visual imagery is initiated in a top-down way; in contrast to the clear onset of a perceptual event, this initiation of imagery may vary substantially between trials . Thus, the onset of imagery decoding is expected to have a smoother rise , and "temporal generalization" would increase a lot .Importantly, this explanation would neither suggest that in imagery complete representations are activated at once, nor that the order of processing steps is somehow altered as compared to perception. Note how also the "dip" in cross decoding at 210 ms is explained by this account, without evoking a more complicated explanation: If the neural representation at 210 ms after onset of the event is just very distinct (for whatever reason), the same signal scattered in time from trial to trial would impair cross-decoding specifically for this very time point . Do the authors think that their data are consistent with this alternative explanation, or are their data points that refute this account?9) To dissociate the temporal uncertainty explanation and the explanations given by the authors, I would strongly suggest additional analyses that try to estimate – and potentially correct for – the temporal uncertainty in imagery-related activation. One such analysis could try to align the imagery trials based on the within-imagery decoding. For example, the authors could perform a leave-one-trial-out analysis where they train a classifier on all trials, and test the classifier on the remaining trial, while shifting the left-out trial in time until the maximum decoding accuracy can be reached . Then for each trial's imagery period, this optimal temporal shift can be used to re-epoch the signal. If the processing sequence is similar and just suffers from temporal scattering, this procedure should significantly improve the cross-decoding accuracy while decreasing temporal generalization. […] The reviewers and editors thought that greater attempts need to be made to make a point with respect to our understanding of imagery. At the moment, this paper is methodologically sound, carefully done, and well written. However, we think it has a very descriptive character, presenting a result that is not entirely surprising  and offering two different interpretations. That's a bit unsatisfying. To also make a stronger theoretical contribution, we think the authors should make a greater effort to disentangle these hypotheses .We agree with the reviewers and editors that the paper would benefit from more focus on the neural mechanisms of imagery.Specifically, we have:- Performed within-category decoding for both perception and imagery (issue 1). We have added an explanation for the difference in decoding accuracy between perception and imagery in the manuscript;- Performed additional analyses on vividness (issues 3 and 4) and have added the results to the manuscript;- Reworked the conclusion to better highlight the contribution of our findings to the field (issue 5);- Added an explanation of the temporal generalization results with respect to task specificity (issue 6);- Connected the temporal cross-decoding results to the N170 (issue 7);- Performed additional analyses to try to accommodate the temporal uncertainty within imagery (issue 8 and 9) and added more discussion on this issue in the manuscript.Essential revisions:1) How does the cross-temporal decoding of faces vs. houses compare to the cross-temporal decoding of faces (resp. houses) in each task. In other words, for each task, how does the cross-category signal variability compare to the within category signal variability in each condition. The reviewers and editors acknowledge that the authors go through a random permutation procedure to identify data-based chance level. Our question pertains to a better understanding of the neural bases of imagery: could it be that overall lower accuracies during imagery is due to a higher variability in within category information? This is a way to address whether the decay in decoding accuracy during imagery is due to weaker representations or increased representational variability.We agree with the reviewers that exploring the relationship between within-category and between-category decoding during both imagery and perception is interesting. However, we optimized the design of the current experiment for between-category decoding. Given that imagery produces a relatively noisy signal, we attempted to maximize our signal-to-noise ratio (SNR).Ideally, there would be only one stimulus per category, which is maximally different from the stimulus in the other category, and we would obtain as many repetitions as possible. However, we also needed participants to stay engaged in the task and motivate them to form detailed mental images. Therefore, they performed a more challenging exemplar identification task.The stimuli in this task consisted of 8 exemplars per stimulus category that were as similar as possible , but the two stimulus sets (faces versus houses) were very different. We had a maximum of 120 trials per category , which gave enough power for low but reliable above chance classification within imagery. In contrast, for instance-based decoding we would have 120/8 = a maximum of 15 trials per class.To illustrate this issue fully, we have run pairwise classifiers on all combinations of stimuli  for the peak decoding latency for perception (~160 ms) and for imagery (~1 s). As can be seen . There will likely be larger variation between trials for a cognitively more complex process.We agree with the reviewers that more explanation for this difference in decoding accuracy is warranted and have added the following to the text:“The generation of a visual representation from a cue thus seems to take longer than the activation via bottom-up sensory input. […] This is also observed in fMRI decoding studies  and is probably due to the fact that imagery is a less automatic process than perception, which leads to higher between trial variation in neural activations.”2) When presenting the temporal overlap between perception and imagery analysis, and Figure 3, the authors mention that the reported results describing that a classifier trained on perception can extract information from imagery data much earlier than a classifier trained on imagery data, might be due to the fact that more trials are available to the trained. This is a crucial point. We strongly recommend that the authors down sample their perception data to make this analysis  more comparable to the data presented in Figure 2. In this absence of this control, it is difficult to interpret the presented result.We thank the reviewers for pointing out this issue. These results were written based on an earlier version of the analysis in which we did not yet apply cross-validation to the cross-decoding analysis, which meant that there were more data in this analysis compared to the within-condition decoding. However, we realized that this could lead to false positives due to autocorrelations in the signal. Therefore, for the final cross-decoding analyses which are reported here, we did apply cross-validation, leading to the same amount of training data as for the within-imagery (and within-perception) analyses. Therefore, the earlier decoding here cannot be explained by a difference in amount of training data.To clarify this point, we have added the following sentence to the Materials and methods section “Generalization across time and conditions”:“For both the temporal generalization as well as the across condition generalization analysis, we also applied cross-validation to avoid overestimating generalization due to autocorrelation in the signals.”Even though the earlier decoding of imagery based on perception could not be due to a difference in amount of training data, it could still reflect differences in SNR between the two analyses. As can be seen from the higher decoding accuracy during perception (see previous point), perception has a higher SNR. Therefore, training (or testing) on perception will result in increased decoding performance. To clarify this point, we have added the following to the text:“This is earlier than classification within imagery, which started at 540 ms after cue onset . Considering the increased decoding accuracy during perception compared to imagery , this difference might reflect an increase in signal-to-noise ratio (SNR) by training on perception compared to imagery.”3) An important additional analysis that needs to be added is the analysis presented in Figure 2C on the high vividness and low vividness trials separately (on matched samples sizes). One expects there to have an overall lower decoding accuracy on low vividness trials. This analysis we expected to directly relate imagery decoding accuracy with the subjective report of imagery vividness. This would a be prior analysis to the one presented in Figure S2, addressing whether more overlap of imagery representation with perceptual representations correlated with higher vividness in imagery subjective experience (further discussed in the next point).We agree with the reviewer that it is important to explore the effects of vividness on the imagery decoding. We have explored the effects of vividness on decoding accuracy more thoroughly and reported the results (see our response to issue 4 below).4) The analysis presented in Figure S2, correlating perception/imagery representational overlap with imagery vividness is a crucial analysis to strengthen the scope of the present paper. It is based onto a LDA, thus assuming a linear relationship between imagery vividness and similarity between perceptual and imagery representations. One can expect other possible relationships between the two: e.g. a one or nothing mapping or a sigmoid type of mapping. We recommend to the authors to explore this differently. One way of doing it would be to perform to separate analysis as in figure 3 for high vividness imagery trials and low vividness imagery trials. One expects a difference to support the subject experience of imagery, though this difference might be localized to some cortical sources .We agree with the reviewers, and have looked at decoding accuracy for high and low vividness separately.increases the accuracy for cue 2 trials and decreases the accuracy for cue 1 trials, irrespective of any imagery. This effect will likely influence the accuracy throughout the imagery trial. Usually, one averages over all trials and thereby cancels out this effect. However, there are more high vividness cue 2 trials than cue 1 trials (probably due to the recency effect). This means that effects of vividness between trials within participants will likely be influenced by this bleed-in. Therefore, we decided to instead only look at group-level effects of vividness.When performing these analyses, we realized that prior to imagery the signal corresponding to the second image will be present more strongly in the data than the signal corresponding to the first image . Since we used a two-class design in which the class of the imagined stimulus either corresponds to the second image  or to the other class , this means that this bleed-in/recency This also means that the analysis that we proposed earlier, in which we correlate the LDA distance to the vividness, might have been influenced by recency and/or bleed-in. We decided to remove that analysis from the paper and replace it by the group-level analysis.N = 12, vividness: 71.64 ± 12.44) and a low vividness  group. We produced the accuracy maps for all previous analyses and plotted the difference between high and low vividness in supplementary figures of the main figures .We performed a median split on the averaged vividness across trials on the group level, which yielded a high vividness . Surprisingly, this also seems to be the case for the accuracy during perception (panels A and B). This might reflect a general attention modulation or a working memory encoding effect. Furthermore, there seems to be an interesting pattern present in the cross-decoding matrix, especially when training on imagery : overlap around 100 ms and around 210 ms during perception (x-axis) seems to be higher in the high vividness group. We have added these results to the paper. It is unfortunate that we do not find any significant effects of vividness. However, as we already mentioned in the Discussion, influences of vividness have only been found in very specific brain areas. It is quite possible that these may become negligible when looking at a whole brain signal. Furthermore, if the signal suffered from temporal uncertainty (see issues 8 and 9), then it would be even harder to find a consistent effect of vividness at specific time points.5) The conclusion states that the 'findings show that imagery is characterized by a different temporal profile than perception.' This conclusion doesn't sound entirely surprising, and makes us think that the authors may be selling themselves short. We think it would be important to highlight what the results actually say about the neural basis of imagery.We agree that we can provide a more explicit interpretation of the neural mechanisms of imagery. Several other reviewer comments relate to this issue and we have tried to accommodate all of them. Furthermore, we extended the emphasis of the paper also to the overlap in time between perception and imagery. We have reworked the Introduction to take this into account as follows:“However, the temporal dynamics of visual imagery remain unclear. During imagery, there is no bottom-up sensory input. […] Second, we investigated the temporal overlap by exploring which time points during perception generalized to imagery.”Furthermore, we have adapted the conclusion in the Discussion as follows:“In conclusion, our findings suggest that, in contrast to perception, at the onset of imagery the entire visual representation is activated at once. […] Together, these findings reveal important new insights into the neural mechanisms of visual imagery and its relation to perception.”6) Furthermore, it might help the Discussion if the authors speculated how specific their results are to the task they chose, rather than imagery per se. In their task, imagery is always preceded by presentation of the exact stimulus to be imagined , which means that participants will still have a lot of information about the imagined stimulus in visual working memory. This would not be the case if, for example, participants were suddenly prompted to imagine a novel object. Could this partially account for the good cross-generalization between perception and imagery? What if there had been a control condition requiring memory but no imagery? Without such a control, how much of their findings may be attributed to visual working memory, rather than imagery?We agree with the reviewers that it is important to speculate about to what extent our results are specific to the task we used. It has previously been reported that overlap between imagery and perception is indeed lower when imagery is cued from long-term memory . Therefore, cueing from long-term memory would have likely also resulted in lower cross-decoding accuracy here. However, this would have been likely been caused by a number of memory-related processes , which were not the focus of the current study. In contrast, our aim was to increase our understanding of the neural mechanisms of visual experience in the absence of bottom-up input and to what extent they are similar to when bottom-up input is present. To do this, we wanted to maximize the similarity between perception and imagery while only varying whether or not bottom-up input was present. This is what we attempted here by using a retro-cue paradigm, in which the participant saw the to-be-imagined item only seconds before. Furthermore, other research has shown that imagery and visual working memory rely on highly similar representations in visual cortex . Accordingly, we do not aim to dissociate imagery and visual working memory here, but rather compare the temporal dynamics underlying visual experience in the presence and absence of sensory input.However, it is possible that the temporal generalization pattern during imagery would be different if we cued from long-term memory, which would point towards different mechanisms for imagery from long-term memory versus imagery from working memory. To further connect our findings to the specifics of the task, we have added the following paragraph to the Discussion:“The large temporal generalization at the onset of imagery might have been partly due to the nature of the task we used. […] Future studies comparing temporal generalization during imagery from short- and long-term memory are necessary to investigate this further.”7) The clear decoding peak around 160 ms seems like it could be related to the N170 or M170 component. Since this component is so well-studied in terms of its role in face processing and its cortical sources in the ventral stream, it seems warranted to discuss this a bit more. Does the fact that the 160 ms peak cross-generalizes well across time and from perception to imagery indicate that face- or house-selective areas in the ventral stream are active at this time and then maintain activation later on, especially during imagery?We thank the reviewers for pointing this out to us. Our findings can indeed be linked very clearly to the N170. Accordingly, we have added the following paragraph to the Discussion:“We observed clear overlap between imagery and perceptual processing around 160 ms after stimulus onset. [...] Furthermore, this time also showed long temporal generalization within perception, indicating that the N170 representations also remain active over time during perception.”8) The authors raise two different explanations for their results, e.g., in the Abstract: "These results indicate that during imagery either the complete representation is activated at once and does not include low-level visual areas, or the order in which visual features are activated is less fixed and more flexible than during perception." However, it is possible that there is a probably less exciting, yet more parsimonious explanation for the pattern of results.Perception and imagery could just evoke the same processing cascade , with two restrictions: (a) Imagery might not "start" as early as perception, thus not involving the earliest stages of perceptual processing. Thus, the onset of imagery decoding is expected to be later than the onset of perception decoding.This third explanation is interesting, but we feel it is not in line with our findings: if there is a clear processing cascade during imagery one would expect a more diagonal pattern for the temporal generalization . Furthermore, one would expect that later time points during perception overlap with later time points during imagery, and the same for earlier time points. Even if the earliest time point that overlaps with imagery is later than the earliest time point during perception that contains information (i.e. ‘imagery starts later’), one would still observe a temporal shift in the overlap of different time points if there really was a processing cascade.late visual representations, then that would refer to the cross-decoding results. We have reworked the Abstract to clarify these different explanations. Furthermore, in the Discussion we explained this point as follows:Alternatively, if the reviewers mean that imagery does not include the earliest time points, and only activates “This absence of early overlap seems to imply that, even though early visual cortex has been implicated in visual imagery, there is no consistent overlap between imagery and early perceptual processing. […] Alternatively, early perceptual activity during imagery may be more brief and variable over time than high-level activation, leading to a cancelling out when averaging over trials.”(b) Visual imagery is initiated in a top-down way; in contrast to the clear onset of a perceptual event, this initiation of imagery may vary substantially between trials . Thus, the onset of imagery decoding is expected to have a smoother rise , and "temporal generalization" would increase a lot .Importantly, this explanation would neither suggest that in imagery complete representations are activated at once, nor that the order of processing steps is somehow altered as compared to perception. Note how also the "dip" in cross decoding at 210 ms is explained by this account, without evoking a more complicated explanation: If the neural representation at 210 ms after onset of the event is just very distinct (for whatever reason), the same signal scattered in time from trial to trial would impair cross-decoding specifically for this very time point . Do the authors think that their data are consistent with this alternative explanation, or are their data points that refute this account?Please see our response to Issue 9.9) To dissociate the temporal uncertainty explanation and the explanations given by the authors, I would strongly suggest additional analyses that try to estimate – and potentially correct for – the temporal uncertainty in imagery-related activation. One such analysis could try to align the imagery trials based on the within-imagery decoding. For example, the authors could perform a leave-one-trial-out analysis where they train a classifier on all trials, and test the classifier on the remaining trial, while shifting the left-out trial in time until the maximum decoding accuracy can be reached . Then for each trial's imagery period, this optimal temporal shift can be used to re-epoch the signal. If the processing sequence is similar and just suffers from temporal scattering, this procedure should significantly improve the cross-decoding accuracy while decreasing temporal generalization.We agree that temporal uncertainty during imagery is a very important issue, which deserves more emphasis in this paper. We thank the reviewers for this suggestion and have run this analysis .We found that the best shifts for each trial were distributed quite uniformly over time. This indicates that either the representation at imagery onset does indeed generalize broadly over time, or that there is a very wide distribution of temporal jitter across trials. If the latter were true and we did identify the onset of imagery in each trial, the resulting temporal generalization matrix should show a more diagonal pattern, indicating changing representations. This is not what we found: when training and testing on the realigned data, we still observed broad off-diagonal decoding. This seems to suggest that there is indeed broad temporal generalization, although it is possible that the decoding model on which we based the onset alignment did not reflect the imagery onset.To further investigate this issue, we aligned the trials based on lagged correlations between the MEG signal, instead of decoding accuracy . Because this analysis does not make use of decoding accuracy for the onset shift, any observed increase in accuracy would be due to an increase in temporal alignment. We did not observe any increase in decoding accuracy after aligning the trials in this way. Furthermore, the broad off-diagonal temporal generalization remained. It is difficult to draw conclusions from this because the accuracy did not increase, indicating that perhaps the single-trial signals were too noisy to get reliable cross-correlations.Finally, we explored the recently developed Temporally Unconstrained Decoding Analysis , which estimates the number of unique states that are needed to describe a neural process, while allowing variation in the exact timing of these states within each trial. We also corresponded with the first author of that paper regarding the method. Unfortunately, in its current form, TUDA is not suited to answer the questions that we have .In conclusion, we cannot confidently dissolve the temporal uncertainty issue with the current analysis tools. We agree that temporal uncertainty is an important alternative explanation for part of our findings. Furthermore, we feel that it is more parsimonious than our earlier suggestion that the order of low-level feature activations may be more flexible during imagery. Therefore, we have replaced that part of the Discussion with the following paragraph:“An alternative explanation for the broad temporal generalization during imagery is that, compared to perception, imagery is less time-locked. […] To fully resolve this issue, future research should systematically explore the effect of temporal uncertainty on different underlying processes and analysis tools need to be developed that can account for variation in temporal dynamics between trials.”


Pantholops hodgsoni) is one of the most endangered mammals native to the Qinghai-Tibetan Plateau. The population size has rapidly declined over the last century due to illegal hunting and habitat damage. In the past 10 years, the population has reportedly been expanding due to conservation efforts. Several lines of evidence suggest that the Tibetan antelope has undergone a demographic bottleneck. However, the consequences of the bottleneck on genetic diversity and the post-bottleneck genetic recovery remain unknown. In this study, we investigate the genetic variation of 15 microsatellite loci from two Tibetan antelope populations sampled in 2003 (Pop2003) and 2013 (Pop2013). A higher level of genetic diversity  was detected in Pop2013, compared to Pop2003 . We observe that despite passing through the bottleneck, the Tibetan antelope retains high levels of genetic diversity. Furthermore, our results show significant or near significant increases in genetic diversity  in Pop2013 compared with Pop2003, which suggests that protection efforts did not arrive too late for the Tibetan antelope.The Tibetan antelope (chiru,  Pantholops hodgsonii) is a member of the Bovidae family (order Artiodactyla) native to the Qinghai–Tibet Plateau (QTP). It is distributed primarily throughout central Tibet, Qinghai, Xinjiang and west Sichuan in China, occupying a total area of approximately 880,000 km2 at an altitude of 3700 to 5500 mth century, with a population size ranging from 500,000 to 1,000,000 individuals during peak yearshttp://www.iucnredlist.org/details/15967/0) and as a Category I species under the Wild Animal Protection Law of China. To protect the Tibetan antelope and restore the population, the Chinese government has set up seven Nature Reserves since 1993The Tibetan antelope or chiru . Placental tissues were collected from the Zhuonaihu Lake area in Hoh Xil National Nature Reserve in 2013 . All samples were washed three times with deionized water, sucked with 95% ethanol and stored at −80 °C. All necessary approvals for collection and experimentation were acquired for the described field study from the Forestry Department of Qinghai Province, China. All procedures were in accordance with the guidelines of the regulations of experiments on animals and were approved by the China Zoological Society.http://pgrc.ipk-gatersleben.de/misa/misa.html) for extracting the microsatellite DNA sequences from genome DNA of chiru . The forward microsatellite primers were labeled by FAM and synthesized by Sangon Biotech (Shanghai). The primers specific for 15 microsatellite loci are shown in A total of 15 polymorphic microsatellite loci were screened. Seven loci  were obtained using the FIASCO methodTaq DNA polymerase and 1 μL DNA template. The PCR programme was set at 94 °C for 2 min, followed by 35 cycles at 94 °C for 30 s, x °C  and Shannon index (I) were calculated by Microsatellite Analyzer V4.05Ho), expected heterozygosity (He) and analyzed the Hardy-Weinberg equilibrium (HWE) by implementing Arlequin V3.5PIC) was estimated by modified PowerStats Worksheet17Θ (theta) = 4Ne × Mu, where Ne is the effective population size and Mu is the microsatellite mutation ratio.Original data were analyzed and manually corrected to validate the accurate peak shape and allele size using GeneMarker V1.75. Micro-Checker was used to validate the availability of genotype data12NA varied from 8 to 21 in Pop2013 (P < 0.001). However, significant deviations from HWE were confirmed only at locus P73 and locus P154 in Pop2003 and locus P17 and locus P73 in Pop2013 after Bonferroni correction (adjusted α = 0.05/15).For the 15 polymorphic loci surveyed, a total of 223 alleles were observed in both Pop2003 and Pop2013, 192 alleles were found in Pop2003, and 197 alleles were found in Pop2013. Overall microsatellite variability was high  in Pop2003, and  Pop2013 . Among aP < 0.05). We therefore excluded this locus in subsequent analysis. Over 14 microsatellite loci, microsatellite loci pairs P1/P9, P9/P78 and P24/P96 were in LD (P < 0.05). However, no LD was detected between these locus pairs after Bonferroni corrections (adjusted α = 0.05/14). In Pop2013, there was significant LD between 55 locus pairs . However, only 22 locus pairs were found to be in significant LD after applying Bonferroni corrections for multiple tests  was 12.92 (range 8–19), the average Ho was 0.832 (range 0.660–0.979), and the average He was 0.810 (range 0.636–0.925). In Pop2013, the average of NA, Ho and He across 14 loci was 13.133, 0.810 and 0.840, respectively; NA varied from 8 to 21, Ho varied from 0.556 to 0.957 and He varied from 0.721 to 0.920. The PIC at each microsatellite locus was always larger than 0.5 (range 0.561 to 0.909), a threshold value considered to be highly informative. Shannon information index ranged from 1.240 to 2.678, which also indicated a high genetic diversity.Microsatellite data of 14 microsatellite loci revealed abundant genetic diversity in both Pop2013 and Pop2003 . In Pop2P = 0.923; Pop2013: P = 0.985). The sign test, standardized differences test and Wilcoxon test all showed a significant heterozygosity excess in either Pop2003 or Pop2013 under the IAM (P < 0.05). However, this was not necessarily indicative of true heterozygosity excess, as the IAM is thought to be a less appropriate model for microsatellites than the SMMThe infinite allele model (IAM), TPM and stepwise mutation model (SMM) were applied when BOTTLENECK was used to test for population bottlenecks in this study. Populations exhibiting a significant heterozygosity excess would be considered to have experienced a recent genetic bottleneck. Under the TPM, the results displayed no genetic bottleneck effect in either Pop2003 or Pop2013 . FurtherHe, PIC, I and Θ at 9 out of 14 loci . In comparison with Pop2003, paired t-test demonstrated that Pop2013 had significantly higher values of average He  and PIC  but differences in average MNA  and Ho  were not found  to 5.305 (Pop2013) over an 11-year period (P < 0.05).A tendency toward recovery of genetic diversity was observed in the Tibetan antelope. Pop2013, compared with Pop2003, exhibited higher values in ot found . There wr period , showingMNA, He and PIC. Although the results are not directly comparable because different microsatellite loci were used, comparisons of the Tibetan antelope to other Bovidae species suggest that genetic diversity in the Tibetan antelope ranks highest 217MNA and He, respectively.Demographic bottlenecks can result in a loss of genetic variation22232425Studies have indicated that factors such as dispersive capabilities30313233353638We conclude that ample genetic diversity may still exist in the Tibetan antelope. Furthermore, the Tibetan antelope has shown a slight increase in genetic variation during the past 11-year period. In this sense, the results of the present study suggest that protection efforts did not arrive too late for the Tibetan antelope and provide molecular evidence for the effectiveness of conservation strategies.How to cite this article: Du, Y. et al. Microsatellite Loci Analysis Reveals Post-bottleneck Recovery of Genetic Diversity in the Tibetan Antelope. Sci. Rep.6, 35501; doi: 10.1038/srep35501 (2016).


The city is a complex system that evolves through its inherent social and economic interactions. Mediating the movements of people and resources, urban street networks offer a spatial footprint of these activities. Of particular interest is the interplay between street structure and its functional usage. Here, we study the shape of 472,040 spatiotemporally optimized travel routes in the 92 most populated cities in the world, finding that their collective morphology exhibits a directional bias influenced by the attractive (or repulsive) forces resulting from congestion, accessibility, and travel demand. To capture this, we develop a simple geometric measure, inness, that maps this force field. In particular, cities with common inness patterns cluster together in groups that are correlated with their putative stage of urban development as measured by a series of socio-economic and infrastructural indicators, suggesting a strong connection between urban development, increasing physical connectivity, and diversity of road hierarchies. Complex networks are a useful tool to investigate the structure of cities and their street networks. Here the authors investigate the shape of travel routes in 92 cities and define a metric called inness which reveals connections between common urban features in cities with similar inness profiles. These activities are mediated by the accessibility of urban spaces depending on the city topography and its infrastructural networks4, a key component of which are the roads. Indeed, different street structures result in varying levels of efficiency, accessibility, and usage of the transportation infrastructure11. Structural characteristics, therefore, have been of great interest in the literature15 and many variants of structural quantities have been proposed and measured in urban contexts, including the degrees of street junctions16, lengths of road segments15, cell areas or shapes delineated by streets13, anisotropies14, and network centrality18. Collectively, these structural properties have uncovered unique characteristics of individual cities as well as demonstrated surprising statistical commonalities manifested as scale invariant patterns across different urban contexts21.The city is an archetype of a complex system, existing and evolving due to the myriad socio-economic activities of its inhabitants22. One way to tease out this connection is to examine the sampling of routes, that is an examination of how inhabitants of a city potentially utilize the street infrastructure. While a number of studies have been conducted on the empirical factors behind the choice of routes27, much remains to be done, in particular, understanding the morphological properties of route choices.While these studies have shed light on the statistical structure of street networks, there is limited understanding of the interplay between the road structure and its influence on the movement of people and the corresponding flow of socio-economic activity; that is, the connection between urban dynamics and its associated infrastructure5.Indeed, the morphology of a route is shaped by the embedded spatial pattern of a city (land use and street topology) in association with dynamical factors such as congestion, accessibility, and travel demand, which relate to various attendant socio-economic factors. Analyzing the morphology of routes, therefore, allows us to potentially uncover the complex interactions that are hidden within the coarse-grained spatial pattern of a city. Furthermore, the morphology also encodes the collective property of routes, including their long-range functional effects. For example, a single street, depending on its connectivity and location, can have influence that spans the dynamics across the whole city (Broadway in New York City for instance)24. On the one hand, one finds an increased tendency of agglomeration of businesses, entertainments, and residential concerns near the urban center, correspondingly leading to a higher density of streets29 and thus attracting traffic and flows toward the interior of the city . Conversely, this increasing density leads to congestion and increase in travel times  thus necessitating the need for arterial roads or bypasses along the urban periphery to disperse the congestion at the core. This has the effect of acting as an opposing force, diverting the flow of traffic away from the interior of the city.In particular, traffic patterns and the shape of routes have been shown to be determined, among other factors, by two competing forces30. We split our analysis between the shortest routes (necessarily constrained by design limitations and city topography) and the fastest routes (representing the effects of traffic and dynamic route sampling), with the former representing aspects of the city morphology, while the latter in some sense representing the dynamics mediated by the morphology (see Methods for details). Specifically, the shortest routes are a function of the bare road geographic structure, while the fastest routes represent the effective geographic structure—a function of the heterogeneous distribution of traffic velocity resulting from varying transportation efficiency and congestion patterns34. To uncover the functional morphology of these two categories of routes, we define a geometric metric, which we term inness—a function of both the direction and spatial length of routes—that captures the tendency of travel routes to gravitate toward or away from the city center. This metric serves as a proxy for the geographical distribution of attractive forces that may be implicit in the sampling of streets  and that otherwise cannot be captured by existing measures. Our analysis represents a step toward the very important challenge of determining the spatial distribution of urban land-use and street topology to balance the inherent negative and positive urban externalities that result from rapid urbanization24.Here, we investigate these competing effects through a detailed empirical study of the shape of 472,040 travel routes between origin–destination points in the 92 most populated cities in the globe, representing all six inhabited continents. Each route consists of a series of connected roads; accompanying information on their geographical location, length, and speed limit retrieved from the OpenStreetMap databaser either correspond trivially to the line connecting them directly, or are degenerate paths that traverse the grid in either direction. Taking the average of the multiple paths cancels any directional bias relative to the center of the grid. Yet, a small perturbation of this regularity can change this neutral feature dramatically, as shown in Fig. 24, that will necessarily affect the patterns seen in fastest paths. Furthermore, the illustration assumes a single center of gravity, as it were, whereas such an effect may manifest itself at multiple scales, resulting in cancellation of any measurable force toward a putative city center.Figure I. Figure s between the pair. We label points lying closer to the center than the geodesic inner points while those lying further away are outer points. For example, in the schematic shown in Fig. I is the difference between the inner area and outer travel areas:To capture whether such an effect manifests itself at the scale of the city, or is indeed neutral due to the “detuning” at smaller scales, we define a metric called the inness 35. On the other hand, as we will demonstrate, the inness, in addition to being a relatively simple measure, encodes the geometric, infrastructural, geographical, and socioeconomic aspects of urban systems.The inness of a node in the road network is a result of aggregating characteristics of all possible routes that pass through that point. Indeed, it reflects the structure of the network since it is a metric influenced by topology and network connectivity. However, in addition to this, it captures the geometric aspects of the network since it is a measure based on the curvature of the roads along a route and encodes structural information at both the global and local scales. In that sense, one can consider it to contain elements of various standard structural network metrics . The fluctuations anticipate a clear inward bias that emerges at a distance of r ≥ 10 km  and the fluctuations are significantly larger. This is indicative of the heterogenous spatial distribution of velocity profiles in the primary roads across cities (due to varying levels of infrastructure), coupled with the fact that they are in general longer than secondary roads.The roads in our dataset are organized in a hierarchical fashion consisting of motorways, primary and trunk roads at the top of the hierarchy and residential and service roads being at the lower levels . Therefore, to account for any bias from variations in travel area within and across cities, we define a rescaled innessThe observed trends of the inness do not take into account the effects of varying travel areas at different distances from the center, or the variation in urban size across the studied cities. To account for these effects, we note that the travel area (averaged across cities) increases roughly quadratically with the geodesic distance r and θ for both the shortest and fastest routes. For instance, inward biases are apparent at distances of 5 km from the city center, an effect suppressed in r and θ. The trend for In Fig. r and θ, r ≤ 30 and 40 ≤ θ ≤ 100. This is likely due to some of the motorways being specialized structures such as ring roads or bypasses that serve as attractors for traffic in the city periphery. This is confirmed by plotting the ratio The distribution of entclass1pt{minimaHaving examined the properties of average directional biases across urban areas, we now turn our attention to the patterns in individual cities. Indeed, as the fluctuations in Fig. To investigate the effect of these differences, we plot each city as a function of the standard deviation and the average of ths Fig. . Most ciIt appears that cities within each region tend to share some common features with respect to these metrics and their inness profiles. Those in the LL group tend to have longer total length of roads, fewer geographic constraints, and strong peripheral connectivity, indicating high levels of infrastructural development. The inness profile is typically neutral (as can be seen for Berlin), indicating no discernible center of the city to which routes are drawn. On the other hand, cities in the HH group tend to have shorter road lengths, limited connectivity in the periphery , and more geographical constraints than the LL group. These cities also have a markedly positive inness profile (shown for Kolkata) suggesting that the navigability of the city passes through a central core. The LH group appears to show a combination of high and low values in terms of the infrastructural metrics, yet is notable in showing markedly higher geographical constraints than the cities in the other regions. This is reflected in a rather peculiar inness profile which manifests itself positively at short distances, yet is negative at longer ranges.To investigate these trends further, we plot the spatial distribution of Next, we focus on the LH group of cities, those with a mix of inward and outward bias in the route patterns. In Fig. Finally, we examine the spatial distribution of ρ between K-means clustering and Jenks natural breaks optimization to partition the cities into three groups with both methods producing nearly identical divisions. , the infrastructure development index , and finally the GDP-per-capita sourced from https://www.lloyds.com/cityriskindex/locations. In Fig. ρ that was used to cluster the cities. In all three cases, there is a clean monotonic decrease in the indicators with increasing ρ, suggesting that inness also encodes information on socio-economic development. A relatively clear pattern emerges whereby the majority of Type I cities are large urban agglomerations with advanced infrastructure and strong socio-economic indicators, Type III cities by and large have comparatively limited infrastructural and socio-economic development, and finally Type II cities share a combination of these features.Advanced levels of infrastructure are typically reflected in improvements across a variety of socio-economic indices. To examine whether the measured behavior of the inness captures any of this, we consider three socio-economic indicators: the productivity index sourced from the city prosperity index (CPI) created by the UN  infrastructural components. In particular, the utilization of street networks captures the complex interactions between people, and the flow of goods and services in urban systems. However, there is relatively limited understanding of this facet as existing macroscopic or microscopic measures are not able to fully capture its properties and associated effects. Part of the challenge is the limited availability of detailed and high-resolution data of dynamics taking place on such networks, necessitating a choice for investigative studies to be made in terms of granularity or scale. In this manuscript we erred on the side of the latter, and conducted a systematic mesoscale analysis of street morphology—representing a proxy for the potential dynamics—through the introduction of a metric that we term inness. The inness encapsulates the direction, orientation, and length of routes, thus revealing the morphology of connectivity in street networks, including the implicit infrastructural and socio-economic forces that may inform routing choices.The average inness pattern points toward the existence of a core–periphery structure across the majority of cities with a high density of streets in the city center with a progressively lower density as one moves toward the periphery. This pattern is particularly seen among Type III cities mostly corresponding to those in Fig. 37. The theory postulates that cities are organized into a hierarchy of cores that perform specific economic functions depending on their position in the hierarchy38. As the core at the highest level of the hierarchy  has the most diverse and complex economic functions, lower cores that perform less complex functions need to connect to the higher core to meet the demand of some of the activities in the higher core. Furthermore, cores at the lower levels of the hierarchy have minimal interactions between them.Interestingly, these qualitative features capture certain elements of the classical hypothesis of central place theory advanced by Christaller39 claims instead that cores at the same level develop specialized and symbiotic functions and thus develop interactions with each other, having the effect of dispersing activity from the center. Indeed, this feature seems to be present in our Type I cities which compose the majority in Fig. 42.An alternative version advanced by LöschA number of cities fall between the spectrum of these two levels of categorization. These are predominantly Type II cities, the majority of which are in the LH group in Fig. 44, with no dominant quantitative definition for what constitutes the type of centricity45. Indeed, the notion of whether cities are monocentric or not depends on the specific feature being investigated. It is notable, however, that some Type I cities considered to be polycentric in terms of our definition are also similarly classified based on completely different metrics such as types of employment and their density patterns45. Though one must be careful with the analogy, given the limited basis of comparison, inness may be also interpreted as a parsimonious and easily measured metric of centricity in cities, given its simple definition and intuitive interpretation.One must note that inness assumes the existence of a unique city center. Cities that show a strong positive value of inness may thus be considered monocentric in the sense that there is a measurable center through which the majority of routes pass, while those with neutral inness are polycentric, in that there is no such center. Centricity in this case has to be interpreted in the morphological sense reflecting the hierarchical organization and spatial patterns of roads. Cities, in general, however, exist in a continuum between mono and polycentricity, depending on how one defines these terms. Generally, centricity in cities has been measured on the basis of locations of high population density or spatial patterns of land useIn summary, it appears that inness as a simple metric encodes a surprising amount of geometric, infrastructural, and socio-economic information of cities. Indeed, the observed connections between inness, road hierarchies, differences in fast and direct routes, geographical accessibility, centricity in cities, socio-economic indicators, and developmental stage, in combination, provide a rather comprehensive picture of urban organization. The results presented here invite more integrated analysis and interpretation, with respect to existing geographical, historical, morphological, and transport-planning studies for individual cities and their development trajectories.latlong.net and the travel routes are sampled according to a choice of OD pairs relative to the center and measured in spherical coordinates . To avoid any sample bias, and to systematically investigate the dependence of route morphology on distance from the urban center, we only consider OD pairs at a fixed radius r.For each of our 92 cities, a city center is defined by referencing the coordinates from r we section the circumference of the circle at intervals of 10° for a total of 36 points . We then vary the radius over the range 2, 5, 10, 20, and finally 30 km (roughly corresponding to a city boundary) and enumerate over all OD pairs by connecting the 36 points at a given radius r for a total of Furthermore, at each Finally, we query the OpenStreetMap API for the suggested travel routes connecting each of the pairs. In fact, for a better characterization of the functional features of the systems  and the role of their hierarchical organizations, we obtained two different kinds of routes between all these pairs: the shortest, based on lengths of road segments, and the fastest that accounts for both the length and the travel time based on flow capacity of the roads . A visual representation of our methodology in segmenting the city is shown in Fig. I is calculated by summing over the areas of the number of polygons in the route by using the shoelace formula, thusn is the number of vertices of the polygon, m is the total number of polygons in the route,  corresponds to the coordinate of j’th vertex of polygon i, and sgn(i) accounts for our adopted convention for inner and outer points.The inness https://github.com/mlee96/inness_research.All data needed to evaluate the conclusions are present in the paper and/or the Supplementary Materials. Additional data related to this paper may be requested from the authors and are also available at Supplementary informationPeer Review File


Pedicularis kansuensis to test whether the current QTP and TSR disjunction is the result of a recent Holocene range expansion involving dispersal across arid land bridge(s) or a Pleistocene range fragmentation involving persistence in refugia. Two chloroplast DNA spacers were sequenced for 319 individuals from 34 populations covering the entire distribution range of this species in China. We found a total of 17 haplotypes of which all occurred in the QTP, and only five in the TSR. Overall genetic diversity was high  and higher in the QTP than in the TSR. Genetic differentiation among regions and populations was relatively low (GST = 0.366) and little evidence for a phylogeographic pattern emerged. The divergence times for the four main lineages could be dated to the early Pleistocene. Surprisingly, the two ubiquitous haplotypes diverged just before or around the Last Glacial Maximum (LGM) and were found in different phylogenetic lineages. The Species Distribution Model suggested a disappearance of P. kansuensis from the TSR during the LGM in contrast to a relatively constant potential distribution in the QTP. We conclude that P. kansuensis colonized the TSR after the LGM. The improbable long-distance dispersal by wind or water across arid land seed flow may well have had birds or men as vector.Quaternary climate fluctuations have profoundly affected the current distribution patterns and genetic structures of many plant and animal species in the Qinghai-Tibetan Plateau (QTP) and adjacent mountain ranges, e.g. Tianshan (TSR), Altay, etc. In this greater area disjunct distributions are prominent but have nevertheless received little attention with respect to the historical processes involved. Here, we focus on  The Markov chain Monte Carlo (MCMC) chains were run for 10,000,000 generations, sampling every 1,000 generations. The combined parameters were checked in TRACER ver. 1.5 [Divergence times for different r. 1.8.1 . In runnr. 1.8.1 , generalrnL-trnF . Becausever. 1.5 . The BayD [ARLEQUIN 3.5 [ARLEQUIN with 1,000 parametric bootstrap replicates. The sum of squared deviations (SSDs) between observed and expected mismatch distribution were computed and P values were calculated as the proportion of simulations producing a larger SSD than the observed SSD. The raggedness index (HRag) and its significance were also calculated to quantify the smoothness of the observed mismatch distribution [To investigate whether populations or groups of populations experienced any population expansion, Tajima’s D  and Fu &D  were calQUIN 3.5 . In addiribution .P. kansuensis as well as during the Last Glacial Maximum , a species distribution model (SDM) was computed using the maximum entropy algorithm implemented in MAXENT 3.3.1 [http://www.worldclim.org/download) along with 34 tested geographical data produced by ourselves were used to estimate the present potential distribution range. The community climate system model (CCSM) [Lastly, in order to estimate the current potential distribution range of NT 3.3.1 . PresentNT 3.3.1  (availabl (CCSM)  was thentrnL-trnF and rpl32-trnL were 830 bp and 770 bp in length, respectively, with a total length of the combined alignments of 1,600 bp. Variable sites showed 40 substitutions and 15 indels. In total, 17 haplotypes (H1-H17) were identified .The aligned sequences of entified , Table 1h) ranged from 0.000 to 0.905 and the YJ (Yajiang) population in the Sichuan Hengduan Mts. contributed the highest value (−3 with a maximum present in the CD2 (Changdu) population in SE Tibet . Thus, the hypothesis of a strong phylogeographic pattern was rejected. In the SAMOVA analyses, FCT values decreased progressively as the values for K number of groups increased from 2 to 10 with no unambigous number of K supported. Also here the hypothesis of a phylogeographic pattern was rejected. Furthermore, the Mantel test revealed a significant correlation between genetic and geographical distances  over all populations. However, a genetic break (barrier) separating the TSR populations from those of the QTP was found with a robustness of 90%  tree calculated for 17 haplotypes from 319 −9 s/s/y was specified in BEAST, while 0.885 to 0.012 Mya for 8.24×10−9 s/s/y  as visualized in P. kansuensis included the east of the QTP as well as the TSR, coinciding well with the species’ extant distribution. By contrast, the predicted distribution for the LGM showed occurrences only in the east of the QTP.The 'area under the curve' (AUC) values for the training and the test data of P. kansuensis individuals belonging to 34 populations in the QTP and the TSR. In comparison with P. longiflora, a congeneric species of P. kansuensis which shares many life-history traits  and has an almost matching distribution range on the QTP with northernmost occurrences in either the Tianshan or the Altay Mts. [P. kansuensis (HT = 0.882 vs. HT = 0.770), though the number of haplotypes found was less than that in P. longiflora  [P. kansuensis was also higher (e.g. Aconitum gymnandrum (HT = 0.739) [Angelica nitida (HT = 0.818) [Cupressus spp. (HT = 0.249 to HT = 0.791) [H. vulgaris (HT = 0.604) [Juniperus sabina (HT = 0.57) [Ligularia hodgsonii (HT = 0.869) [P. kansuensis haplotype diversity was lower in the TSR than in the QTP (HT = 0.753 vs. HT = 0.880). Unlikely, the high level of total genetic diversity can be attributed to a short life-history trait such as longevity or the outcrossing breeding system of P. kansuensis. Considerable gene flow among populations and regions may have played a role in shaping the genetic structure, as deduced from the low levels of genetic differentiation among populations in the entire study area (GST = 0.366), the QTP (GST = 0.368), or the TSR (GST = 0.301)  . When co= 0.739) , Angelic= 0.818) , Cupress= 0.791) , H. vulg= 0.604) , Juniper = 0.57)  and the = 0.869) –37, desp= 0.301) .P. kansuensis, our results, i.e. high genetic diversity within population and low population differentiation, suggest relatively frequent seed exchange among populations. The seeds of P. kansuensis, however, have no obvious morphological adaptations to wind, water or animal dispersal [P. kansuensis [K groups of populations, the comparison of GST and NST values showed no significant difference  and only 2.52% of the molecular variance could be contributed to differences among the two regions  Mya time window that coincides with the early or middle Pleistocene, suggesting that P. kansuensis withstood the extensive climate changes during the Quaternary. During this period, the QTP had experienced four major glaciations [P. kansuensis in the QTP, affected its divergence events, and shaped its phylogeographic structure, just as reported in other plant species [in situ through the Quaternary [P. kansuensis might have survived in known refugial areas at SE QTP (e.g. Hengduan Mts.) and the edge of NE QTP (e.g. Qilian Mts.). However, the results of mismatch analyses  of the QTPG is significant higher than the TSG (HT = 0.880 vs. HT = 0.753)  in the TSR . All fivy region ,25. The = 0.753) . By this the QTP , the NW  the QTP . At thatP. kansuensis from the TSR during the LGM (The species distribution model (SDM) results show an absence of  the LGM . This in the LGM , Fig 3.P. kansuensis has survived on the QTP throughout the LGM. The present day disjunct distribution in the Qinghai-Tibetan Plateau and the Tianshan Region is likely the result of multiple bird or human assisted long-distance seed dispersal events crossing the arid land of Tarim Basin after the LGM, particularly from the northeastern fringes of the QTP to the Tianshan Mts.Based on phylogeographical and species distribution modeling analyses, we propose that S1 Table(DOCX)Click here for additional data file.


Meriones unguiculatus) even though they were all established recently from the same source. We also show that genetic diversity in allegedly “outbred” colonies of nonmodel rodents  varies considerably from nearly no segregating diversity to very high levels of polymorphism. We conclude that genetic divergence in isolated colonies may play an important role in the “replication crisis.” In a more positive light, divergent rodent colonies represent an opportunity to leverage genetically distinct individuals in genetic crossing experiments. In sum, awareness of the genetic diversity of an animal colony is paramount as it allows researchers to properly replicate experiments and also to capitalize on other genetically distinct individuals to explore the genetic basis of a trait.Nonmodel rodents are widely used as subjects for both basic and applied biological research, but the genetic diversity of the study individuals is rarely quantified. University-housed colonies tend to be small and subject to founder effects and genetic drift; so they may be highly inbred or show substantial genetic divergence from other colonies, even those derived from the same source. Disregard for the levels of genetic diversity in an animal colony may result in a failure to replicate results if a different colony is used to repeat an experiment, as different colonies may have fixed alternative variants. Here we use high throughput sequencing to demonstrate genetic divergence in three isolated colonies of Mongolian gerbil ( We also discuss the possibility of leveraging the inescapable genetic drift in small mammal colonies to identify differentiated genetic markers for use in genetic mapping and association studies.If laboratory gerbils are inbred, fewer are needed to achieve statistically significant results and maintain a breeding colony, but any results obtained may not be widely generalizable to other strains or species . Given tcirca 2005. In 2014, Dr. Leila Abbas established a new colony at the University of Sheffield from the Charles River Ltd. Tumblebrook stock and at the same time took over care and housing of three to four pairs of animals from Edinburgh, with both stocks maintained separately. In 2016, we took delivery of 12 new Tumblebrook animals from Charles River  to establish the Bangor colony. We also received five animals from each of the Edinburgh  and Sheffield  stocks. All three groups were maintained in isolation in Bangor, except for a single testcross between an Edinburgh female and a Sheffield male. All animals were housed in accordance with EU and Home Office animal care regulations, and experiments were reviewed and approved by the Bangor University Animal Welfare and Ethical Review Board.Mongolian gerbils are listed in Annex 1 of European Union (EU) Directive 2010/63/EU and must therefore be purposely bred for scientific research. The majority  gerbils used in the EU are derived from the Tumblebrook Farm stock and many academic institutions in the UK, and elsewhere, maintain their own colonies derived from these animals. We analyzed animals from three of these colonies, and to avoid confusion we refer to each colony by the name of the city in which it was first established: Edinburgh, Sheffield, and Bangor. The Edinburgh colony was established by Dr. Judith Allen at the University of Edinburgh 1 Edinburgh × Sheffield offspring and was immediately snap frozen in liquid nitrogen after each animal was killed as part of routine colony management. DNA was extracted with the Qiagen DNeasy Blood and Tissue Kit and treated with RNase according to the manufacturer’s instructions. Extracted DNA was shipped to the Beijing Genome Institute (BGI) (Hong Kong) for library preparation and sequencing. Uniquely barcoded 100-bp, paired-end, genotyping-by-sequencing (GBS) libraries were prepared with the 5-bp cutter ApeKI, pooled, and sequenced on a single lane of Illumina 4000  from the output of cstacks and to it we aligned the raw reads with bwa mem  from those in the autosomal cluster (slope = 1). The intercept was chosen to remain fairly conservative near the origin; that is erring toward labeling a true X-linked tag as an autosome rather than labeling a true autosomal tag as X-linked. All remaining tags were annotated as autosomal. The populations script was used to generate diversity metrics and F statistics across the genome for autosomal, X-, and Y-linked markers. Genotypes were called only for individuals with >10× coverage (-m 10) and only for SNPs in the first 90 bases. We blacklisted SNPs in the final two bases because of an unusually high number of SNPs on those bases . Finally, we evaluated genetic similarity and population structure with the SNPRelate package in R  sequencing data from deer mice  as well as the VCF file containing the locations of all SNPs are available as Table S1. Average heterozygosity at autosomal variant sites is 0.447 and is slightly higher on the sex chromosomes  is from our colonies, we counted the number of differences between our GBS reference and the genome. Full-length alignments were found for 674,342 (93%) of our reference sequences when aligned to the genome. We found 47,223 single-base differences in these aligned regions, far more than the SNPs that segregate within the colonies we assayed. This pattern is consistent with the known population history of laboratory gerbils: the Charles River colony, from which our animals originate, was rederived from a United States colony from which the DNA for the genome was supplied.st metrics between the colonies suggest a high overall similarity between Bangor and Sheffield (Fst = 0.069) and identify Edinburgh as an outlier with an Fst of 0.235 when compared with Bangor and 0.352 when compared with Sheffield. The structure analysis also suggests little overall differentiation between Bangor and Sheffield, and finds that Edinburgh is slightly more divergent, though still very similar   and the M. musculus: π = 0.0054; M. domesticus: π = 0.0102 (Although genetic diversity in laboratory gerbils (π = 0.0059) is lower than that captured in the mice of the Collaborative Cross (π = 0.0289; = 0.0102 , exceed = 0.0102 . It is aWe have demonstrated that appreciable genetic diversity segregates within Tumblebrook Farm-strain Mongolian gerbils. Our findings are contrary to earlier reports suggesting that diversity may be as low as, or lower than, laboratory mouse colonies . These rPhodopus hamsters have been referred to as outbred and maintained in large colonies (Laboratory-maintained rodent colonies are often small due to the costs and space needed for maintenance of many animals. With such small populations, genetic drift plays an important role in determining the standing level of variation. Drift can be expected to increase genetic differentiation between colonies through time, especially given the population bottleneck that often occurs when a colony is established or moved to a new location. Knowledge of levels of genetic diversity in an institutional colony is therefore vital for correct colony management; for example, colonies . Howevercolonies , and so i.e., There are two major ramifications of the loss and partitioning of genetic variation in laboratory colonies. First, animals from the same original outbred stock may respond very differently to an experiment if they come from different isolated colonies. Many articles state that diversity in gerbils is quite low, one even suggesting that smaller error bars in laboratory individuals than wild-caught individuals are due to the lower genetic diversity i.e., . Althoug1 offspring in the PCA (The second important ramification of high genetic drift in laboratory colonies is that although diversity will be lost in any single colony through time, across multiple isolated colonies much of the original diversity may be preserved. This is not a new idea and there are major ongoing efforts using multiple inbred strains to capture the range of natural diversity . By inte the PCA  all suggDespite being derived from a relatively small number of founders and experiencing repeated bottlenecks over the past 80 years in captivity, the Tumblebrook Farm strain of Mongolian gerbils does not have low levels of genetic diversity. However, we have shown that genetic drift in small institutional colonies has increased differentiation and such drift may affect the reproducibility of experiments. For those doing animal research, the choice of whether to use inbred or outbred animals depends on the specific question being addressed . For inswww.g3journal.org/lookup/suppl/doi:10.1534/g3.117.300495/-/DC1.Supplemental material is available online at Click here for additional data file.Click here for additional data file.Click here for additional data file.