On 2017-08-11 03:55:26, user Angélica TB wrote:

Very interesting that DCC is among the genes with SNPs. <br /> We published this work last year regarding the DCC gene. <br /> http://www.sciencedirect.co...

On 2018-07-22 23:16:15, user Emin Orhan wrote:

I believe randomly connected deep networks can also behave in this 1/n fashion if the scaling of their weights is chosen properly. You could thus perhaps calculate a probable effective depth vs. weight scale region for your neural recordings. Here's a reference that might be helpful for this purpose: https://arxiv.org/abs/1606....

It would also be interesting to see how this scaling behavior changes over development. I would expect the scaling to be closer to criticality early on during development and to become smoother later on.

On 2019-06-11 09:18:01, user Jens Staal wrote:

Very nice work! Exciting to see some non-mammalian MALT1 work.

One little note: We did see some phenotypes in neuronal silencing of MALT1 in C. elegans, Our results were however opposite to yours, so probably because we used silencing.

https://www.frontiersin.org...

On 2020-09-21 06:49:10, user Tim Fischer wrote:

Published at: <br /> SAP '20: ACM Symposium on Applied Perception 2020<br /> https://dl.acm.org/doi/10.1...

On 2018-04-07 19:26:18, user Gerome Breen wrote:

So you may wish to also see our paper analysing GWAS data for gene sets from single cell seq of neurons that showed some robust associations <br /> https://www.biorxiv.org/con... <br /> In summary "Genes associated with intelligence implicate pyramidal neurons of the somatosensory cortex and CA1 region of the hippocampus, and midbrain embryonic GABAergic neurons."

On 2018-10-18 14:21:42, user Brendon Watson wrote:

I am very interested in this topic but i have some concerns about this paper - having nothing to do with the computational work necessarily but which leave me with concerns about the details of the actual link between biology and analysis.

• First IT neurons in the Svoboda study aren't interneurons, they are intratelencephalic (see intro section).
• Further at least a minimal description should be made about PT, IT, PT_U and PT_L neurons should be given so that readers can understand to what kinds of cells this work relates

• It should be described that the cells in the datasets used were optogenetically tagged and that that validates this set of analyses. I'd state this at least 2 times in different places, if not more in fact as a major anchor of this work and how people should see and read it.

• Related to formatting - there is indeed a recent standard in neuroscience journals to put results before methods, but in those cases we add some degree of explanation of methods used into the results so readers understand the overview of what the results relate to. In this case all of those important details are in methods. I'd put methods first in this paper.

On 2020-11-14 20:52:28, user Brad Wyble wrote:

This is now published at NBR: https://www.sciencedirect.c...

On 2023-12-16 07:35:32, user jonny saunders wrote:

what up, would love to use this dataset and analysis as a teaching example for a new undergrad, but unfortunately https://github.com/AllenIns... doesn't exist :(

On 2019-04-19 10:46:08, user Laurentius Huber wrote:

Below as submitted to the Journal

500 Word summary sectionThe manuscript entitled “Resolving multisensory and attentional influences across cortical depth in sensory cortices” describes a layer-fMRI study with audio and visual stimuli and it investigates layer dependent signal changes across different attention and modality conditions. The main conclusion of the study is that cross-modal activity modulates the deeper layers, whereas attentional differences modulate the superficial layers. The fact that this can be measured noninvasively in humans, will be of great interest to a large research field. And upon some revisions, I ultimately recommend its publication with great enthusiasm.

The novelty and strengths:<br /> -> While some other groups are also currently working on it, I believe this is the first manuscript of layer-dependent analyses of multi-modal integration.<br /> -> I believe I have not seen any layer-fMRI manuscript that combines so many different brain areas (including PT, which is new for layer-fMRI) and so many different task conditions in one study.<br /> -> The authors developed a novel analysis methodology of interpreting the layer-profiles as a combination of linear ‘slopes’ and ‘constant’ offsets that allow straightforward summary statistical tests across task conditions.<br /> -> The data-acquisition methodology is technically sound and appropriate to address the research questions. Like in previous studies of that group, they use the most advanced imaging hardware, sequences and imaging protocols. Without being too advanced that it would require additional method-validation-studies.<br /> -> The statistical results are shown very honestly in violin diagrams for all conditions in and all participants. And data will be shared.

The weaknesses:<br /> -> I feel the task design might have been pushed it a bit too much. There are as many as 6 task conditions with subtle differences. Thus, either one of the condition differentiations does not have so many trials to average across compared to comparable layer-fMRI studies. As a result, multiple different tasks conditions needed to be averaged together and the main conclusions are based on effects that (almost) disappear in the noise level.<br /> -> I believe the clarity of the manuscript can be improved. Each Figure has up to 20 sub panels, whereas most of them show insignificant effects thats are cannot be used to support the main conclusion. It took me quite a while to filter out the relevant information.<br /> -> I believe the way how the analysis is conducted and the data are presented could benefit from more discussions on the limits of their interpretability. I am hesitant whether I can interpret the shape parameters and decodability profiles as measures of neural activity in a way that the main conclusion is the most plausible explanation for all the depicted results.

More detailed comments are given below:

Detailed comments section<br /> -> I found it very hard to understand what the figures are supposed to tell me. Each Fig. shows up to 20 panels and I applaud the author's sense of completeness not to sweep anything under the carpet. However, in my naive understanding most of the panels show more noise than the signal that is used to draw the conclusions. If I understand it correctly, the second and third panels in Fig. 3A,4A are the main results and the basis of the final conclusion, no? Maybe it makes sense to highlight them? Maybe it makes sense to combine them in the same graph to be able to visually see the difference? Maybe it would help to explicitly state in the figure captions, what to look out for. Is there any structure in Figs. 3B,4B that one should be able to see? Or is it supposed to look like random scattering? Is there a reason why the strongest effects are outside the ROIs (dark blue in insula 3Bi, and dark blue in parieto-occipital area 4Bii)?

-> I do not really understand the line of reasoning behind the percentile plots. The authors write on line 802 “Under the null-hypothesis the laminar BOLD response profile for the predicting contrast (e.g. [V-fix]) in a vertex is unrelated to its laminar BOLD response profile for the predicted contrast (e.g. [AV-A])”. Maybe it would be clearer, if they explicitly state the underlying assumptions? In rudimentary understanding from my own data, I would guess that the major reason for vertex-to-vertex variability is (in this order): 1.) registration errors, 2.) curvature-dependent segmentation errors, 3.) heterogeneous occurrence of principal veins, 4.) curvature dependent occurrence of veins, 5.) orientation dependence to B0, 6.) signal leakage of kissing gyrus, 7.) coarse topographical neural representation variation, 8.) fine scale columnar neural representation. All of these modulators (aside of the last ones) should be the same across all 6 task conditions, no?

-> It is very clear that the authors carefully looked at the data in many different ways and performed many tests. While I admire the thorough approach, I am wondering about the risk of false positive significance scores. Altogether, I counted 112 reported p-values! And the discussion then focuses on those that are significant. E.g. main conclusion is based on Fig. 4A (p=0.101, p=0.013) compared to Fig. 3A (p=0.691, p=0.542). Given that the results are not reproducible in control task condition (see next point), is it possible that this can be explained by false positives?

-> None of the effects in the main conclusion are reproduced in control task conditions: The attention effect in visual areas is not reproduced by attention effects in auditory areas. The cross-modal modulation in auditory areas is not reproduced in visual areas (neither in size, direction, nor shape of layer signals).<br /> Similarly, none of the effects in the main conclusion are reproduced across the alternative control analysis approaches (shape parameters vs. decodability): Crossmodal layer-profiles in A1 and V1 (Fig. 3A) look very different for B-parameters compared to the respective decodability values (zero, constant, decreasing, zero).<br /> This is partly discussed and explained as different neural mechanisms for the different areas, which makes sense to me. However, it also makes me wonder about how generalizable and impact-full the results are for other people in the field. The conclusions seem to be confined to very specific tasks, in individual areas for unique analysis methods only.

-> Given that the crossmodal and attentional modulations are not the same across tasks, I am a bit puzzled how to interpret any profiles shown in Fig. 2-4. These profiles refer to averages across all attention conditions or averages across all sensory modulations. This means that there are partly competing effects averaged together in one profile. E.g. the first panel in Fig 3A ([AV-A]ATTv,Atta) is a flat line. Should I interpret this as a result that visual stimulation does not affect auditory cortex? Or should I interpret this as: paying attention to auditory tasks has an exactly opposite effect compared to paying attention to visual tasks. Such that they cancel each other out and average to zero?<br /> Maybe it would be cleaner to focus on individual attention effects here?

-> While I appreciate the novelty and the elegance of quantifying the layer-profiles as “constant” and “slope”, I found it hard to interpret the resulting statistical significance scores. <br /> I would feel much more comfortable recommending the manuscript for publication, if there would be a clearer idea what these estimates mean and how they refer to layer-specific activity.<br /> I believe it can be justified, to present these new findings with this new (un-validated) novel methodology in the same study. However, I feel that the implications of the shape parameters should be discussed in more depth. What is the physiological and neuroscientific basis of them? Are they independent from each other or are the just different ways of quantifying the same thing. Namely, overall signal magnitude that always is stronger in superficial than deeper layers. <br /> What’s the rational to use these shape parameters? Is there an advantage over direct interpretations of signal changes in superficial vs deeper layers? <br /> Is the ‘constant’ shape parameter just a result of segmentation and partial-voluming errors with WM? Naively, I would expect that the BOLD signal should go back to baseline at the GM|WM border.

-> I feel that using variations in ‘decodability’ as a measure of layer-dependent activation could benefit from additional discussions too. The interpretation of ‘decodability’ variation only works if they are in a similarly SNR regime between ‘ceiling-level’ and ‘chance-level’. It is known that the superficial layers are suffering from larger portions of physiological noise compared to deeper layers. And thus, I am having a hard time interpreting the flat line in the second panel in Fig. 3A as constant neural activity without venous leakage. Couldn’t it be that there is just a higher noise level in the superficial layers that limits the decodability superficial layers?

-> One of the main conclusions of the study is that attention modulated activity in superficial layers of the auditory ROI. (line 245: “significant positive linear effect in A1 and PT” Fig. 4A). I don’t really understand how this ‘significance’ is derived. With the naked eye, the first panel in Fig. 4A looks like a scaled version of the first panel in Fig. 2A. Thus to me, attention seems to enhance all the layers?!? Is the inferential statistical test conducted based on the difference of the differences? Or am I missing something? And the signal increase towards upper layers is more superlinearly increasing for in Fig. 4A?

-> The B-values are estimated with a canonical HRF (line 683). While this is not completely uncommon in layer-fMRI, it comes along with challenges of the interpretability. It is known that the HRFs are different across cortical depths, tasks, and brain areas. Could this bias some of the results, and how?

-> line 599. I assume it’s supposed t mean 36 mm?<br /> -> line 256x240: It would be relevant to state which one is the phase encoding direction.<br /> -> line 602: missing full stop.<br /> -> line 609: it would be relevant to state if the same shim values were used across days.<br /> -> line 664: It would be relevant to state how many spatial resampling steps the data went through and how this might affect the effective resolution.<br /> -> line 440: double full stop.<br /> -> line 145: A-> B<br /> -> line 151L: B-> C

Additional Data section <br /> I believe it is customary for layer-fMRI studies to provide the reader with some sample images of tSNR, segmentation borderlines over EPI data, and registration between anatomy and EPI. Given that this study is one of the very very few studies that did not incorporate manual corrections of the segmentation (line 623), I am sure it would be appreciated by the reader to see the quality of the segmentation.

It is common in the field that ROIs are manually outlined (line 637). However to avoid the possibility that such manual steps are used to only select those areas that support the hypothesis, it is customary that such manual delineations are shown (e.g. in supplementary material).

Since the authors offer to share the data upon request after the publication. It is not necessary to provide these data now?

Congratulations on the nice manuscript,

Renzo<br /> https://media1.giphy.com/me...

On 2021-03-13 10:37:56, user Neha Khawaja wrote:

Sorry, but where can I find figure 5? It was referenced in the results, but I cant see it in the paper.

On 2019-05-21 15:52:27, user Martino wrote:

I disagree with the presence of a "decoder" at the end of the visual pathway in figure 1, unless you're a "Cartesian theatre" kind of person.

(Also, coarser, not courser).

On 2019-05-25 13:54:19, user Jesper Sjöström wrote:

Very nice, but it seems to me that this paper should also cite Annecchino et al., 2017, Neuron 95, 1048–1055, http://dx.doi.org/10.1016/j...

On 2017-10-07 13:02:43, user Yaroslav Halchenko wrote:

if I got it right from a brief review<br /> -why not tested on that Smith 2011 iirc simulated dataset?<br /> - how compares to the results obtained using other algorithms, eg by IMaGES+LOFS from Ramsey, Glymour et al, which showed one of the highest scores on that simulated one

On 2023-03-15 16:57:59, user mannam varun wrote:

Full paper published in SPIE journal of Biomedical Optics (JBO):

https://www.spiedigitallibr...

On 2021-03-04 13:55:35, user Johannes Franz wrote:

Dear Tim van Mourik, Peter J. Koopmans, Lauren J. Bains, David G. Norris, Janneke F.M. Jehee,

Thank you for posting your manuscript as a preprint. We enjoyed reading and discussing it in our layer fMRI journal club (Maastricht University). We would like to provide a few comments compiled from our discussion that we hope will be of use to you.

The manuscript describes a layer-fMRI study with a spatial attention task. The behavioral protocol follows a long tradition in the psychophysics of spatial attention, and the layer fMRI predictions stem from a well-established literature on the neurophysiology of attentional modulation in visual cortex studied with single units. Thus, we think that the experiment is perfectly suited for applications with layer-fMRI. The acquisition and analysis procedures include cutting edge methodologies and both data and analysis code is claimed to be openly available.

We believe a large readership will appreciate your investigation of the effect of spatial attention on laminar BOLD activation profiles in an orientation discrimination task, as well as your intention to drive the young field of laminar fMRI towards more thorough reporting of analysis choices and consequences. Furthermore, we are excited about the pipeline being publicly available.

In this study you show, similar to previous findings, an increase in BOLD response for attended regions, with and without visual stimulation. Yet, unlike previous studies, you did not find an effect of spatial attention across layers.

We believe the manuscript could be improved along the following points:

1.) Data are hard to access:<br /> We fully agree with the lead author in his agenda that open sharing of data is mandatory for modern research. We think this is even more essential for replication studies that do not see the same layer-dependent effects compared to previous studies. Only when the data are available, the community can employ their own set of tools and expertise to help tease out potential layer-specific attention effects and/or potential reasons for a disagreement between studies.<br /> Given the authors' stated support for open science, and the fact that the manuscript mentioned more than 5 times (at most prominent places) that all data are openly available, we were surprised how difficult it was to get access to the data. Many of us did not succeed in getting access to the MRI data straightforwardly. After reading IT manuals on how to use webdav.data, setting up our ORCID settings from scratch, and after requesting a temporary Donders account, we succeeded to download the data of the single participant that is provided.<br /> The time course data are much easier to access. However, we were disappointed that those data do not refer to MRI data per se, but rather refer to model fits, which are highly processed, and upsampled to a temporal resolution that is three times that of the actual fMRI time series. The manuscript might benefit from adding a few details about the shared time course data.

2.) Details on data acquisition:<br /> The acquisition of the functional data is described in one single sentence (line 354f). To aid the importance of reproducibility, we believe this section would benefit from further explanations. <br /> 2a) E.g. application of GRAPPA 8 is rather liberal and unconventional in the field. In fact, some of us first thought it was a typo. Maybe the authors can convince the reader that this is an appropriate choice of acquisition by explaining how this could be achieved (CAIPI = 1/4) and/or reporting basic quality metrics (e.g. tSNR) that allow judgement of the g-factor penalty.<br /> 2b) We were a bit surprised by the application of partial Fourier in both phase encoding directions. We believe that this might be an important piece of information to be reported in the manuscript and might help explain why no high-resolution attention effect was observed. As the MR-physicists in the author list know much better than us, the application of partial Fourier is based on the point-symmetry of the Hermitian k-space. This means that for applications of partial Fourier in both directions, it is not possible to synthesize (recover) the missing outer k-space data that represent the high spatial frequencies. With PF 6/8 for resolutions of 0.827x0.827x0.80mm^3, this results in an effective resolution of 1.15mm in the diagonal direction. Given that V1 has a cortical thickness of at most 2.5 mm, it is perhaps not surprising that the authors failed to observe differences between deep, middle, and superficial cortical layers with this effective spatial resolution.

3.) Interaction of attention and orientation:<br /> Maybe the manuscript could benefit from including a (supplementary) figure of the behavioral data. What was the effect of the attentional manipulation on orientation discrimination? Were the behavioral effects similar in magnitude to previous studies of spatial attention?

4.) Units of signal change:<br /> It was not clear to us why the values on the y-axis in Figure 1 and 2 are so small compared to the percent signal change reported in Figure 3? Do the arbitrary units in Figure 1 refer to the same scaling across task conditions and time steps?

5.) Surprisingly short inter-trial intervals:<br /> We were surprised by the unconventionally short duration of the inter-trial intervals. We wondered whether this timing introduced an HRF-bias that might have confounded the characterization of layer-specific effects. Specifically, it is likely that the shape, and possibly, the linearity, of the HRF varies with cortical depth (Figure 2). Each trial has an average length of 4.7s, followed by a variable inter-trial interval of length 1 to 2.5s. Due to the variable hemodynamic response function across cortical depth (Yacoub 2006, Petridou 2017; full citation attached below), it is expected that the depth-dependent response interacts non-linearly for trials that follow in such quick succession. As such, the accumulating signal in the superficial layers might not return back to baseline as fast as the signal in the deeper layers. In addition to the draining effect, signals might be carried over to the next trial in a depth-dependent way. Specifically, the superficial signal might not only reflect processes across cortical depths from the current trials, but also processes from previous trials while the signal at lower depth could be expected to have less ‘memory’. This layer-dependent bias of non-linear HRF might diminish the attention effect in superficial layers more than in other layers. We feel that this concern could be addressed by additional control experiments with very long inter-trial intervals.

Yacoub E, Ugurbil K, Harel N. The spatial dependence of the poststimulus undershoot as revealed by high-resolution BOLD- and CBV-weighted fMRI. 2006:634-644. doi:10.1038/sj.jcbfm.9600239

Petridou N, Siero JCW. Laminar fMRI: What can the time domain tell us? NeuroImage. http://dx.doi.org/10.1016/j.... Published 2019.

6.) The performance of the spatial GLM is unclear:<br /> Figure 3 has a very appealing layout that nicely conveys the relevant information. When comparing Figure 3 (main analysis with spatial GLM) to Figure 3-Figure supplement 4 (analysis with interpolated laminar signal) we noticed that the effect of ascending/draining veins (the slope of the lines) is comparable in both, if not flatter in the latter case, which is counter-intuitive (the spatial GLM should mitigate the impact of the vascular bias from pial vessels). We would be very interested in a discussion of how the spatial GLM is expected to handle potential carry-over effects between trials such as described in Point 5.

7.) Voxel selections:<br /> We appreciate the additional analyses summarized in Table 1, repeating the analysis including different numbers of vertices. Specifically we wondered whether not using a selection threshold on the vertices of the main experiment but instead purely relying on the ROI definition of the retinotopic localizer would lead to similar conclusions as when imposing an activation threshold. Is there a danger that a statistical activation threshold in the voxel selection could have resulted in the final layer profiles coming from patches of the cortex that are more dominated by ascending and pial veins (blooming)? Could the lack of localization specificity from those veins be responsible for the lack of layer-specific attention effects? In fact, if we could access the data, we would be interested in repeating the analysis and specifically excluding the voxels with the largest responses (which the authors have focused on), as these are the very voxels that are most likely to be contaminated by a vascular bias.

8.) Failed to replicate or a new research question?<br /> We were a bit surprised about the article type this manuscript is listed as. In previous public communication (e.g. workshops and thesis) with the lead author, the study was phrased in the context of a replication attempt. However, the article type chosen here is “New results”, as opposed to BioRxiv’s other available categories: “Confirmatory Results”, or “Contradictory Results”. <br /> While we believe that either category would be of interest to a large readership, we feel that the manuscript would benefit from an in-depth discussion of previous layer-fMRI studies that could indeed replicate a spatial attention effect in superficial layers. Maybe the authors can use these studies to estimate the expected effect size of the layer-specific attention effect in a power analysis explaining why the study at hand might not have been able to detect such modulations. Example studies are listed below:

Liu C, Guo F, Qian C, et al. Layer-dependent multiplicative effects of spatial attention on contrast responses in human early visual cortex. Prog Neurobiol. 2020;(July):101897. doi:10.1016/j.pneurobio.2020.101897

Gau R, Bazin P-L, Trampel R, Turner R, Noppeney U. Resolving multisensory and attentional influences across cortical depth in sensory cortices. Elife. 2020;9:1-26. doi:10.7554/elife.46856

Hollander G De, Zwaag W Van Der, Qian C, Zhang P. Ultra-high resolution fMRI reveals origins of feedforward and feedback activity within laminae of human ocular dominance columns. Neuroimage. 2020. doi:10.1101/2020.05.19.102186

Klein BP, Fracasso A, van Dijk JA, Paffen CLE, te Pas SF, Dumoulin SO. Cortical depth dependent population receptive field attraction by spatial attention in human V1. Neuroimage. 2018;176(October 2017):301-312. doi:10.1016/j.neuroimage.2018.04.055

Lawrence SJD, Norris DG, de Lange FP. Dissociable laminar profiles of concurrent bottom-up and top-down modulation in the human visual cortex. Elife. 2019:1-28. https://doi.org/10.7554/eLi....

Marquardt, I., De Weerd, P., Schneider, M., Gulban, O. F., Ivanov, D., Wang, Y., & Uludag, K. (2020). Feedback contribution to surface motion perception in the human early visual cortex. ELife, 9, 1–28. https://doi.org/10.7554/eLi...

9.) How can a large number of participants account for head motion?<br /> Lastly, while we agree that it can be useful to include larger sample sizes for population statistics we fail to follow the reasoning: “For example, at a resolution this high, even the smallest movement of the participant may cause additional blurring of the data, with potentially detrimental effects on the signal-to-noise ratio. For this reason, we collected data from 17 participants”. It could be argued that to reduce the influence of measurement error, high-resolution fMRI experiments should repeatedly sample a small number of subjects. Given the large number of participants, we would be especially interested in a discussion of individual results, in relation to individual motion estimates.

Stylistic suggestions:

Line 10: “Directing spatial attention towards a particular stimulus location enhances cortical responses at corresponding regions in the cortex.” -> We would suggest to specify that BOLD responses increase with attention, not necessarily neural responses.

Line 80: ‘histiological’ -> histological

Line 356: ‘T2*-weigthed’ -> T2*-weighted

Line 367: ‘3200 m’ -> 3200 ms

Figure 3 and supplementary figures -> Could you elaborate on the gray diamonds?

We would advise the authors to consider changing the color code in all time series figures. E.g. The two types of red and the two types of blue in Figure 1 are indistinguishable. Should the reader infer which line refers to which condition based on the magnitude of the response? If so, it could be mentioned in the caption.

The two types of red in Figure 2 are hardly distinguishable.

In Figure 1–Figure supplement 1, the two panels have no description that distinguishes them. We assume one refers to right and one refers to left hemispheres? It is puzzling why the unattended (blue) line in the right panel has a larger response than the attended (red) line. Is it possible that trials are not labeled correctly for one of the hemispheres? Specifically, does the attention label reflect ‘attention to the left’ instead of ‘attention to the contra-lateral side w.r.t. hemisphere'?

Overall, we find this work presents an important contribution to the field by attempting to replicate a previously observed effect and promoting a replicable pipeline. We hope that our thoughts and comments will be helpful. We are looking forward to seeing this manuscript published.

With kind regards,<br /> Sebastian Dresbach, Lonike Faes, Johannes Franz, Omer Faruk Gulban, Renzo Huber, Miriam Heynckes , Eli Merriam, Alessandra Pizzuti, Yawen Wang

On 2022-11-08 15:11:05, user Baptiste Lacoste wrote:

Very cool work! Makes a lot of sense considering time scales, with longer responses involving GPCR-induced NOS activation (e.g. by Ach in endothelium). Wondering what contribution endothelial NO has vs. neuronal NO... Would be nice to dissect major cell types (endo, astro, neurons). I suggest changing the title to "Neuronal nitric oxyde is not (...)" just to stay on the safe side. My two cents!

On 2016-05-02 08:22:27, user Tom Wallis wrote:

Great, thanks for the comments! They certainly will help to improve the manuscript; I will consider them in our next revision.

On 2019-11-30 05:40:23, user Andrew York wrote:

I'm concerned that this work may not be appropriate for this venue.

On 2018-07-03 15:17:25, user Dan Tracey wrote:

This paper has a nice series of experiments that convincingly demonstrate that sensory input from the tarsi on the legs of Aedes aegypti mosquitos promotes the repellency of the well-known compound DEET when these mosquitos feed on human skin. Preventing the tarsal contact by providing very small patches of DEET nicely shows that the gustatory neurons on the mosquito feeding appendage (the stylet) are not sufficient to mediate avoidance of DEET. As well, preventing tarsal input by sealing the legs with glue blocks repellency with DEET- further suggesting that input from the tarsi is necessary for DEET avoidance. This is true even though other experiments indicate that DEET can function as a gustatory feeding repellent. For instance DEET blocks feeding on sucrose in a CAFÉ assay and it also blocks feeding when mixed with blood in a glytube assay. The key findings in the study include some interesting differences that are found between the actions of DEET and several bitter tasting compounds. First, DEET can prevent feeding when it is only present on the surface of the glytube feeder while quinine cannot. Second, two bitter compounds (quinine and lobelline) fail to inhibit feeding when applied to human skin. Importantly, all of the experiments are carried out in orco mutant mosquitos thus ruling out olfactory inputs in this repellency mechanism.

The major conclusion of the study is that DEET inhibits feeding through a mechanism that involves contact with tarsi (and not the stylet), and that this effect cannot be replicated with the tested bitter tasting compounds. These conclusions are well supported by the data.

The paper is short and “sweet” yet there are areas that the paper could be substantially improved to help readers that do not have extensive expertise in the chemosensory system of insects.

For instance, the manuscript is almost completely lacking in any citation of the prior literature on the study of tarsal taste. Instead, the authors create their own definitions: restricting the use of word taste to describe sensation of the mouthparts. In doing so, they attempt to redefine the tarsal taste system as not a taste system. In places, the manuscript is written in a way that suggests to the reader the discovery of a new chemosensory system that was previously unknown. Yet, the prior literature on tarsal taste is substantial and goes back for at least 100 years. The field started with the early discovery that butterflies can taste the sweetness of nectar with their feet and it is the sugar touching the feet that triggers the proboscis extension.

It may be more interesting for a novice reader to simply learn that insects of all kinds are very well-known to taste with their feet and mosquitos are no exception. This suggested change to the manuscript would also allow the authors to provide a more thorough and rigorous treatment of the prior literature.

Indeed, the prior literature might provide a potential explanation for how DEET could be acting through a tarsal “bad taste” system even if quinine and lobelline are not capable of blocking feeding through the tarsi in Aedes aegypti mosquitos.

For instance, Ling, Dahunakar et al, while in the Carlson lab, performed detailed electrophysiological recordings of the tarsal gustatory sensilla in Drosophila. They found that some bitter compounds are able to activate tarsal gustatory neurons without activating gustatory neurons on the labellum. Other bitter tastants activate both labellar and tarsal gustatory neurons and so on. Flies are also known to have a diverse repertoire of bitter taste receptors and the expression patterns of particular receptors acts as a combinatorial code that produces diverse responses to various chemicals across classes of bitter taste sensilla. It seems likely that a similar molecular logic will be found to be in place in the tarsal and stylet taste receptors of mosquitos.

As always, the authors should be applauded for their important work studying the relatively intractable Aedes system. Yet, it is not clear that it is warranted for them to conclude that the repellent actions of DEET cannot be adequately studied in organisms such as Drosophila. The possibility that DEET acts on the tarsal taste system of flies has not been ruled out and remains a likely possibility. Indeed, the broad spectrum action of DEET makes the identification of its conserved molecular target(s) (whatever they may be) of even greater importance.

On 2020-03-06 00:40:20, user Simon McMullan wrote:

This is a really great innovation - thanks for your hard work. However, the Electric Field sensor you have used (Plessey Semiconductors, PS25251) is no longer manufactured. Can you suggest an alternative component?

On 2022-06-16 02:18:49, user Toshiya wrote:

If you look at Fig.4F, what is interesting is that kindled WT obviously made fewer errors than Sham. Can kindling make animals smarter?

On 2017-03-29 18:06:47, user AdamMarblestone wrote:

-"Biologically inspired protection of deep networks from adversarial attacks" https://arxiv.org/abs/1703....<br /> -"Mapping of a non-spatial dimension by the hippocampal–entorhinal circuit" http://www.nature.com/natur...

On 2017-09-06 01:23:54, user AdamMarblestone wrote:

-"Seeing faces is necessary for face-domain formation" http://www.nature.com/neuro...

On 2017-04-26 18:39:19, user AdamMarblestone wrote:

-"Dynamic Nigrostriatal Dopamine Biases Action Selection"<br /> http://www.cell.com/neuron/...<br /> -"Learning: Neural networks subtract and conquer"<br /> https://elifesciences.org/c...<br /> -"Spontaneous emergence of fast attractor dynamics in a model of developing primary visual cortex"<br /> https://www.nature.com/arti...

On 2017-11-08 14:04:13, user AdamMarblestone wrote:

-"Cortical microcircuits as gated-recurrent neural networks" https://arxiv.org/pdf/1711....

On 2017-05-13 00:10:33, user AdamMarblestone wrote:

-"Rapid Integration of Artificial Sensory Feedback during Operant Conditioning of Motor Cortex Neurons" http://www.sciencedirect.co...

On 2017-08-10 18:43:52, user AdamMarblestone wrote:

-"Sequential neuromodulation of Hebbian plasticity offers mechanism for effective reward-based navigation" https://elifesciences.org/a...<br /> -"Neurons in the Basal Forebrain Project to the Cortex in a Complex Topographic Organization that Reflects Corticocortical Connectivity Patterns: An Experimental Study Based on Retrograde Tracing and 3D Reconstruction" https://academic.oup.com/ce...<br /> -"Organization and somatotopy of corticothalamic projections from L5B in mouse barrel cortex" http://www.pnas.org/content...

On 2017-03-13 15:58:44, user AdamMarblestone wrote:

-"Dopamine reward prediction errors reflect hidden-state inference across time" http://www.nature.com/neuro...

On 2017-12-29 15:51:34, user AdamMarblestone wrote:

-"The blueprint of the vertebrate forebrain – With special reference to the habenulae" https://www.sciencedirect.c...<br /> -"The locus coeruleus is a complex and differentiated neuromodulatory system" https://www.biorxiv.org/con...<br /> -"Locus coeruleus input to hippocampal CA3 drives single-trial learning of a novel context" http://www.pnas.org/content...

On 2017-09-26 11:38:29, user AdamMarblestone wrote:

-"Learning with three factors: modulating Hebbian plasticity with errors" http://www.sciencedirect.co...

On 2017-12-04 21:03:33, user AdamMarblestone wrote:

-"New approaches to deep neural networks" https://agi.io/2017/12/01/n...

On 2016-11-21 15:01:32, user AdamMarblestone wrote:

-"Cerebellar learning using perturbations" http://www.biorxiv.org/cont...

On 2017-07-26 16:36:26, user AdamMarblestone wrote:

-"Biologically plausible learning in recurrent neural networks reproduces neural dynamics observed during cognitive tasks" http://www.biorxiv.org/cont...

On 2018-01-05 04:08:18, user AdamMarblestone wrote:

-"Dendritic processing of spontaneous neuronal sequences for one-shot learning" https://www.biorxiv.org/con...

On 2017-10-18 21:20:56, user Nick Schurch wrote:

Figure 2 panel H, I don't really believe the negative correlation between mRNA fold-changes and m6A fold-changes. The r^2 = 0.05 is very small and without being quoted with confidence intervals its difficult to say if it really is inconsistent with 0 despite the small p-value (which is expectedly small given the large number of data-points in the plot). It'd definitely be worth quoting 95% confidence intervals for the R62 values....

On 2017-10-09 17:40:47, user Jesse Gomez wrote:

The paper actually has gone through a round of peer review, and the above manuscript represents the revised version.

On 2021-06-10 15:24:54, user Murat Bilgel wrote:

Very impressive sample size and a monumental study! I think you will find this recent paper "The Brain Chart of Aging" by Habes et al. relevant: https://alz-journals.online...

On 2018-03-23 13:01:33, user Stephen Van Hooser wrote:

This is an amazing paper. For discussion: didn't David Marr propose this combination of sustained and transient cells as a mechanism of direction selectivity? (Marr and Ullman 1981)<br /> http://rspb.royalsocietypub...

On 2020-09-03 15:43:40, user Jeremy Hall wrote:

Excellent paper from Anstey et al combining in depth behaviour with phsyiological analysis of the fear system in a rodent model of autism. Shows the relevance of basic neuroscience understanding to complex neurobehavioural disorders. Nice.

On 2019-03-18 20:12:47, user Amanda wrote:

This is really promising. Yet, does this also work in female mice?

On 2020-01-14 11:16:11, user Dilawar Singh wrote:

This is a very interesting study. I wish it was published earlier so I could have cited it in our paper https://elifesciences.org/a... where we showed (computationally) how subunit exchange synchronizes clusters of CaMKII. Because of which a giant hyper-stable bi-stable switch can emerge in the spine.

On 2020-06-23 02:37:23, user rex wrote:

will this be able to cure social anxiety and depression?

On 2020-05-28 15:37:43, user Zyotirmoy Talukdar wrote:

If this is possible , I’m manipulating brain with a neuralink chip..then It will also possible that someday we can digitally extract the data we store inside our brain (memories) , manipulate perception and consciousness..this will be huge step towards immortality

On 2020-02-16 10:11:18, user Shaohua Shang wrote:

Is the neural signals data from the rat in the paper above published? where can we get this neural signals data of rat?

On 2022-06-14 23:48:19, user Brad Friedman wrote:

This dataset looks pretty interesting, and I like the basic analysis provided in the manuscript. Figures 3B and 3C (PCA of smRNA-Seq and CAGE-Seq) appear to be identical. Can the authors please double check this?

On 2019-03-30 17:28:09, user Carsten Allefeld wrote:

Dear Satoshi,

I think it is great that you pursued the possibility to use other order statistics instead of the minimum, which you are right has clear drawbacks!

I haven't read the whole paper yet, but I do have one request. On page 3 you appear to cite me verbatim with the words “there is at least one participant in the population that has a greater-than-chance D-Acc”. I haven't written those words, and I don't completely agree with them (because the population can in principle be infinite, a single subject is not necessarily enough to drive significance). Would you please either make clear that you paraphrase me, or use one of the formulations I actually used in the paper? Here are some candidates:<br /> – "there are people in which there is an effect"<br /> – "there are some people in the population whose fMRI data carry information about the<br /> experimental condition"<br /> – "there are some subjects in the population in which there is an above-chance effect"<br /> – "there are some subjects in which there is an effect"<br /> Thank you!

If you are interested in more detailed feedback on your paper, feel free to contact me; my email is in my paper.

Best,<br /> Carsten

On 2018-09-26 21:26:47, user Gopikrishna Deshpande wrote:

Have you reproduced the DMN results using PCC-seed based functional connectivity? If so, I would appreciate if you could share those results

On 2020-07-19 00:22:01, user Spirit Chaser wrote:

Big if true. I can't imagine what the hospital situation would really be like if the blockage of the feeling of pain was not blocked for asymptomatics like the study claims..

On 2016-02-09 23:01:41, user roedert wrote:

test - do not approve

On 2021-09-16 20:15:22, user CP wrote:

"The Kdm2b neural conditional mutant mice were generated by mating Ash1L floxed mice with Nestin-cre mice" is this a typo?

On 2023-03-28 15:25:13, user Theo Cheung wrote:

Is it possible to access the data used? thanks !

On 2020-05-26 00:19:33, user Naixin Ren wrote:

The MATLAB code of the method is available on github: https://github.com/NaixinRe....

On 2018-02-13 17:23:59, user Nikolaos Robakis wrote:

The paper reports a novel interaction of PS with its APP substrate that controls the presynaptic expression of the Ca2+ sensor protein Syt7 and, ultimately, presynaptic facilitation and replenishment of release-competent vesicles.

Importantly, the g-secretase activity of PS regulates presynaptic facilitation by controlling the levels of APP derivative APP-?-CTF shown here to regulate the levels of Syt7. APP-?-CTF is a substrate of g-secretase and accumulates in the absence of g-secretase activity. Thus, g-secretase regulates synaptic functions by controlling the neuronal levels of an APP derivative. In summary, this is an interesting paper that should attract many readers.

On 2018-02-15 19:39:07, user Zhao Xuan wrote:

The paper reported that presenilin, the mutations of which cause familial Alzheimer’s disease, is necessary for synaptic vesicle recruitment or replenishment underlining presynaptic plasticity. The authors further determined that in the absence of presynaptic presenilin, the axonal expression of the calcium sensor synaptotagmin-7 (Syt7) is decreased. Re-expression of Syt7 at the presynaptic site is able to rescue the synaptic deficit caused by loss of presenilin. The data also suggested that presenilin/g-secretase plays a role in reducing the level of an APP derivative (APP-bCTF) to maintain the normal level of Syt7.

This paper combines biochemical assays (western blot and immunoprecipitation) with electrophysiology and uses some very nice optogenetic tools to achieve light-inducible and tissue specific knock-out/expression of target proteins in the hippocampal DG granule cells to examine mossy-fiber-CA3 synapses specifically.

The paper is important in linking the fundamentals of synaptic transmission with potential mechanisms for neurodegenerative diseases.

On 2018-06-04 17:22:09, user X.J. Wang wrote:

What about white matter? Glial cell of the Oligodendrocyte lineage are especially sensitive to hypoxia

On 2019-11-21 22:42:02, user Evelin Cotella wrote:

This article has been accepted with a different title in Psychoneuroendocrinology. https://doi.org/10.1016/j.p...

On 2015-11-27 13:39:24, user John Smith wrote:

If you've got any questions or thoughts on the paper then please don't hesitate to get in touch with us.

If you've got any genetic, transcriptomic or other gene sets you'd be interested in applying the method to then let us know.

On 2017-03-09 13:22:33, user Ashutosh Pandey wrote:

Respected fellow,i am intrested to do PhD work in medical imaging. i have recently completed in master in nuclear mediicne .can anyone guide me ? i humbly request to you please guide me . have a good day

On 2018-10-02 16:35:07, user markus wrote:

I posted an open review of this interesting article here: https://markusmeister.com/2....

On 2014-06-01 02:23:33, user Savraj Grewal wrote:

The process your paper addresses is better described as either mRNA translation or protein synthesis. Strictly speaking protein translation doesn't exist

On 2015-05-28 11:34:57, user Tauber Lab wrote:

That's a great paper and FLYGLOW is a brilliant (literally) idea. I already can think about a few interesting experiments using this system. In principle, you should be able to monitor group of flies freely roaming in an open (flat) space, not just glass tubes. I wonder though if you can still detect a reliable signal if you drive expression only in the brain.... Also, it would have been useful to show some recording of clock mutants.

On 2021-09-14 11:46:32, user Dr. Kumari Aditi wrote:

This paper has been accepted for publication in the journal Experimental Biology and Medicine.

On 2023-01-25 16:11:28, user Amanda Sierra wrote:

The reviewed paper is published in Autophagy 2023: https://pubmed.ncbi.nlm.nih...

On 2023-04-25 18:26:21, user Ephraim Trakhtenberg wrote:

Accepted for publication in Brain Research

On 2022-10-26 01:12:31, user Rek Var wrote:

Revised manuscript available here and here

On 2021-02-09 07:54:02, user drummondmcculloch wrote:

Hi everyone, Drummond McCulloch here. The purpose of this preprint is to take questions and suggestions about the paper, please leave any such comments below and I'll be sure to get back to you. Thanks!

On 2016-06-11 21:24:53, user Alan Kay wrote:

A video of the procedure has been posted at https://youtu.be/rH5aOc7ZKjY

On 2021-03-11 08:40:36, user Renzo Huber wrote:

Dear Partricia Pais-Roldán, Seong Dae Yun, Nicola Palomero-Gallagher, and N. Jon Shah,

We have discussed the manuscript in the layer-fMRI journal club in Maastricht on Tuesday March 2nd 2021.

We admired the rich information presented in the figures and the careful validation of the acquisition and preprocessing analysis in the motor cortex before the application of functional connectivity.

We would like to share our thoughts and discussions about this exciting and comprehensive work.

1.) Details on acquisition methods. <br /> We were struggling a bit to fully understand the details about the data acquisition procedure used here. Given the use of the unconventional keyhole imaging, we were wondering if the manuscript would benefit from a few more details:<br /> -> How many lines did the keyhole contain? How big was the keyhole compared to the periphery?<br /> -> What was the segmentation factor of the periphery of k-space (based on the ISMRM presentation, we assume it was 3)? <br /> -> We think it would be appropriate to explicitly state the temporal resolution of the layer-specific high spatial resolution (outer k-space lines). We believe it is 10 s as opposed to the mentioned TR of 3.5 s.<br /> -> If we understand EPIK correctly, it has a different effective bandwidth (in the phase-encoding direction) for the keyhole compared to the periphery. Thus, the low-resolution information in image space suffers from different distortions than the high-resolution information. The complex-valued interference of both of those different components can result in some artifacts. Like signal cancelation in some of the cortical layers compared to others (See Fig. 3 of this ISMRM Poster: https://cds.ismrm.org/prote... "https://cds.ismrm.org/protected/20MPresentations/pdfs/3863.pdf)"). Does this need to be considered for layer-fMRI interpretations? <br /> -> We are aware that the manuscript refers to citation [49], an ISMRM abstract by Yun et al. However, we were afraid that the poster also did not give us enough information to reproduce the sequence.

2.) Application of phase regression. <br /> While we were excited about the way the authors paid attention to draining vein contaminations in the GE-BOLD signal, we were wondering about the limits of the method of phase regression.<br /> -> As far as we understand it, it is based on the assumption that the large draining veins are temporarily uncoupled from the micro-vascular signal in the parenchyma (e.g. delay of 1-2 sec). Given the effective temporal resolution (sliding window TR) of 10s at high spatial resolutions used here, we believe this assumption is not completely valid. Could it be that the phase regression is more appropriate for short TRs compared to long TRs? <br /> -> As far as we understand the phase regression method, it can solely remove vein effects of macro-vessels that have a caliber of the voxel size and thus, evoke a net voxel phase effect. Hence, we believe the regression analysis can barely remove the effect of the pial veins. With the voxel size used here, the phase regression can barely remove the effect of pial veins. The methods, effectiveness for much smaller diving veins might be very limited. <br /> The effectiveness of phase regression to remove large vessels only, seems to be supported by the results presented in Fig. S2. The two selected veins are outside GM and should not have a large effect on the layer-fMRI signal. The selected voxel V3, in the superficial layers (which is expected to be dominated by contaminating diving veins), does not seem to benefit from phase regression. <br /> -> The application of partial Fourier is based on the assumption that k-space is Hermitian-symmetric i.e. that there is no local phase variation. Thus, using a pF factor of 5/8 means that the effective spatial resolution of the phase image is 25% of that of the magnitude. Hence, we were wondering how appropriate it is to quantify the phase information with the application of partial Fourier imaging. It sounds to us like estimating the phase by assuming that there is no phase. We were wondering how applicable the phase regression method is, when there is limited high-resolution layer-specific phase information with partial Fourier. <br /> -> It is usually not too straight forward to extract phase images from multi-coil arrays without risking phase singularities. Maybe the manuscript would benefit from additional descriptions of how the individual RF channels were combined.<br /> -> We were wondering whether the supplementary information could benefit from additional explanations, about the absence of temporal or spatial phase wrapping (lines 73 suppl.). With an echo time of 22 ms, this would suggest that no voxel of time point exceeded more than 45 Hz off-resonance. Which is surprisingly little for 7T.

3.) Layer-specific interpretation of regional homogeneity (ReHo). <br /> As far as we understand, the ReHo is a measure of resting state connectivity that is not very locally specific, i.e. it is a measure of similarity of the time courses of each voxel in the near vicinity of the voxel. Due to the 3-voxel diameter for the 19 used neighbors kernel, we were wondering whether any layer-specific information might be lost?!? We believe that this might be also the reason why the ReHo variation is almost unchanged across cortical depth (Fig. 4c). The layer-specific ReHo modulation seems at least an order of magnitude smaller than the modulation across areas. We believe the manuscript would benefit from an additional figure showing a representative ReHo map to convince the reader that it is not inherently smoothed by the kernel size of three voxels, rendering layer-specific interpretations impossible.

4.) Interpretation of correlation as a measure of layer-specific connectivity.<br /> We were discussing a lot what layer-dependent correlation values might be affected by. Namely, local density of venous vessels, remaining physiological noise and CSF pulsation in superficial voxels, layer-dependent noise level. <br /> Do the authors interpret that the increased connectivity of superficial layers of sulci could be coming from the increased likelihood of pial veins in sulci compared to gyri?

5.) Connectivity maps <br /> We admired the ICA maps in Fig. 5 and Fig. S5. Unfortunately, they were too small to really appreciate the localization specificity and layer-dependent stripes of connectivity. A larger version of those maps would be clearer and more convincing.

6.) Minor/Stylistic comment on layer sorting.<br /> We were a bit confused about the presentation of layer-fMRI data across Figs. 2-4. <br /> E.g. in the inset of Fig. 2b surfaces close to WM have small values, whereas the surfaces close to CSF have large values. This is in contradiction to the x-axis of Fig3. k, which shows layers with small values close to CSF (left) and layers with large values close to WM (right). The x-axis in Fig. 4b is inverted to that. Maybe it would help the clarity of the manuscript to use a consistent layer-labeling.

7.) Temporal frequencies are differently represented for high and low spatial resolutions.<br /> We had a hard time fully grasping the impact of the EPIK sampling scheme for temporal correlation analyses. In EPIK, the low spatial resolutions (key hole) are more frequently sampled than high spatial resolutions. This might have an effect on the functional correlation. E.g. the high frequencies do not have the layer-specific localisation specificity as opposed to the low frequencies. Could this explain the surprisingly flat resting-state layer profiles in Figs. 4b-c, 5d, compared to the less flat task activations (slow block-design) of layer plot in Fig 3j-m?

8.) y-axis scale of “connectivity”<br /> We had a hard time understanding the quantification of connectivity in the panels of Fig. 5. <br /> The dynamic range of the connectivity strengths in Fig. 5b are about an order of magnitude smaller than the correlation values in panel a. The connectivity values in Fig. 5c) are partly larger than 1. This seems to suggest that it doesn't refer to correlation anymore. We could not understand how this “connectivity” metric is computed. Your manuscript might benefit from adding a definition of this “connectivity” metric.

9.) Layer-dependent SNR might affect thresholded connectivity values in Fig. 6. <br /> Fig. 3f depicts that superficial layers have a substantially stronger MR signal compared to deeper layers. In the thermal-noise dominated regime of sub-millimeter voxels, this signal intensity is expected to be directly translated to tSNR distributions. Thus, we would expect that any functional modulations of (task or resting state) would exceed the statistical detection threshold in superficial layers before they extended the detection threshold in deeper layers. Could it be that the superficial bias of the connectivity modulations in fig. 6 are solely explainable with SNR and (remaining) venous biases? After all, based on low-resolution connectivity and coherence reports, it would be expected that SMA and M1 are functionally connected both during rest and during task?

10.) We feel it would be appropriate to include Polimeni 2010 (ISMRM) into the reference list. While we are aware that this is ‘just’ an abstract, in our understanding of the layer-fMRI history, this work started the field of laminar connectivity. <br /> Polimeni, J., Witzel, T., and Fischl, B. (2010a). Identifying common-source driven correlations in resting-state fMRI via laminar-specific analysis in the human visual cortex. In Proc Intl Soc Mag Reson Med, volume 18, page 353.

We hope that our thoughts on the manuscript are perceived as helpful. And we are looking forward to seeing this manuscript published in a journal and we are looking forward to learning more about the continuation of your exciting work.

With kind regards,

Sebastian Dresbach, Lonike Faes, Johannes Franz, Omer Faruk Gulban, Renzo Huber, Miriam Heynckes, Alessandra Pizzuti, Till Steinbach.

On 2019-07-20 03:52:59, user Joseph D wrote:

I think looking at the distribution of hormones and other potential changes in the blood via MS would be an interesting thing to do in a follow-up. I wonder if 1. the electrical signal in such proximity to the brain/ the recording process itself might cause a release of hormones 2. If there is a significant difference in serum and 3. if this has an effect on growth. Could just run some samples on a mass spec, see what comes up. They've probably already done something like that but it would be interesting to see the results!

On 2016-05-27 22:48:45, user Stephen M. Fiore, Ph.D. wrote:

~ Very nice piece. Joshua Brown from Indiana University took a similar, but less detailed, approach for an article he published a couple of years ago in Frontiers in Neuroscience (see "The tale of the neuroscientists and the computer: why mechanistic theory matters" -- http://journal.frontiersin.... "http://journal.frontiersin.org/article/10.3389/fnins.2014.00349/full#B11)").

On 2024-08-26 13:04:08, user Phil? Like the Groundhog Phil? wrote:

This article has now been published at Brain Communications<br /> https://academic.oup.com/braincomms/article/6/4/fcae256/7729977

On 2018-05-16 20:33:40, user Jacqueline DeRose???? wrote:

Very interesting paper. Results are consistent with my doctoral studies using microdialysis to measure in vivo basal forebrain ACh release with EEG across arousal states https://www.physiology.org/... and http://www.jneurosci.org/co...

On 2024-07-11 10:58:55, user ?? ? wrote:

This manuscript is now published:<br /> https://www.nature.com/articles/s41467-024-49455-y

On 2020-02-16 17:50:24, user Abdulbaki Agbas wrote:

This preprint is now published in a peer-reviewed "Journal of unexplored medical data" DOI: 10.20517/2572-8180.2019.08

On 2023-12-15 00:27:19, user Catherine Carr wrote:

Please note that the current view in J Neurosci does not include the final proof corrections.

On 2019-12-16 18:19:13, user Erin Landry wrote:

We are a team consisting of graduate (Erin Landry) and undergraduate students (Liam McParland, Zachary Milot, Nexhat Mucka) conducting a comprehensive review of your work as part of our Neural Circuits (NE598) course at Boston University. Thank you for posting your work on BioRxiv, and we hope you find our comments helpful!

Microglia are necessary for normal functional development of adult-born neurons in the olfactory bulb

Janelle Wallace, Julia Lord, Lasse Dissing-Olesen, Beth Stevens, Venkatesh Murthy

Summary/Merits<br /> Understanding the role of microglia in the development of adult-born granule cells (abGCs) of the olfactory bulb (OB) during adult neurogenesis is critical to larger conversations about neural regeneration and degenerative processes. In this paper, the authors first examine the interactions of microglia and abGC spines and found that microglia preferentially interact with mushroom spines over filopodial spines. Next, the authors moved on to study the effect of microglia ablation on odor responses of both developing and pre-existing abGCs. Results of these experiments showed that developing abGCs have reduced odor responses while odor responses of pre-existing abGCs are unaffected. Finally, the authors perform electrophysiological experiments in acute slices to find changes in abGC synapse development when microglia are absent. Results show that, in the absence of microglia, abGCs develop smaller spines and produce smaller responses to excitatory input, which strongly argues that microglia play a critical role in the proper development of excitatory synapses on developing abGCs. The experimental setup is extremely well-developed, and generally the data is presented in ways that lend credence to the writer’s theories. Overall, this is a very good paper. The use of the principal component analysis and microglial gating were interesting, although more could have been done with their results. Interesting and well-supported techniques like these are always welcome. Further merits of the paper include the inclusion of injection timelines in figures and the quality of dendrite images. Together, these factors made most of the figures extremely easy to follow. Despite the strong argument overall, there are some portions of the paper which could use additional work to strengthen reader understanding and communication of experiment results. These are highlighted in subsequent sections along with our specific recommendations for improvement. We hope you benefit from our observations as you continue to investigate the growth and specification of neural circuits in olfaction.

Major Criticisms<br /> Overall, the experiments presented in this paper are well-structured and convincing. The major criticisms that we have are not major in the rewriting that they require or the substantial changes to your environmental paradigm that they indicate, but in that they are those most detrimental to the clear communication of your results. Most can be resolved through small changes. One global problem is a lack of clarity in the timeline of adult abGC genesis. More information is needed for readers to understand how long it takes after acquisition of a new odor for granule cells to mature. This will allow readers to follow the logic of the subsequent treatments. As the purpose of scientific literature is to communicate findings in a way that simultaneously shows the logic of the experimental workflow, this oversight needs to be addressed. Similarly, the reasoning behind shifts in timing for PLX/control chow introduction relative to lentivirus injection should be more explicit. At times, it is difficult to know which paradigm is being used without having to reference accompanying figures. Having the timeline graphics in each figure is effective, but this information should also be communicated in the written presentation of information.

The classification of mushroom and filopodial spines was far too subjective, given that differential microglia interaction preferences is the key finding of figure 1. The three steps of manual selection of the best image to analyze, manual delineation of spine heads in Fiji, and classification of spine heads by visual inspection allow for human error at multiple points in the classification process. More quantitative classification and analysis are necessary to convince the reader that the stated findings are true. Matlab or other processing techniques can be used to find the outline the dendrite and spine throughout the Z-stack based on fluorescence intensity and create a 3-D rendering of each spine. These renderings can then be analyzed to determine spine metrics, including length, maximum radius, radius along the length of the spine (as absolute radius and as a percentage of the maximum radius), and total spine volume. A classifier could then be built to determine labels for each spine based on adjustable thresholds for these metrics, such as total spine volume and percentage decrease below the maximum radius. This allows for comparison of classifier outputs to known data on mushroom and filopodial spine morphology and distribution in abGC dendrites. Additionally, the classifier could be tested on a ground truth data set, where mushroom and filopodial spines are identified histologically, to generate a receiver operating characteristic (ROC) curve to show the quality of the classifier. After the reader is convinced that the classifier is adequate, then perform perform the same analyses done in figure 1; supplemental analyses with varying classifier thresholds would further convince the reader. Similar analysis of abGC spines when microglia are ablated would be very beneficial to the paper.

Sample sizes for some component experiments could be larger, but resource constraints are hard to work around. The number of dendrites observed is consistently adequate, but the paper would have been improved by using more total mouse subjects. Additionally, the specific developmental time window for the mice in each sub-experiment should also be clear. Currently, mice are described in the methods as being between 8 and 12 weeks old at the beginning of the experiment. It would be better for all to begin at the same age, but, if this is not possible, the age independence of abGC attenuation should be verified. Otherwise, readers could attribute the dendritic changes that researchers attribute to microglial ablation to groups with imbalanced age distributions. Generally, this is a confounding variable that should be avoided. If it is the case that the mice within each sub-experiment are the same age, thus removing this variable, that should be explicitly stated.

The final, most major criticism of the paper lies in the statistical power of the most important takeaway. Researchers interpret the data in figure 5D to show that PLX-treated mice have a shifted distribution to favor smaller spine head volume. This preference is not statistically shown by comparing the means of PLX-treated and control groups, with a p of 0.13. However, researchers state in their discussion that “the dampened responses we observe are likely a consequence of reduced spine volume and weaker excitatory inputs in neurons that develop in the absence of microglia”. Such a claim is not well supported by their experimental data. Further analysis of spine morphology and electrophysiological experiments are necessary to make this claim. Considering that the authors place such an emphasis on differential microglia interaction preferences between mushroom and filopodial spines, these spines should be classified and analyzed to the same extent suggested above. Mini EPSCs (mEPSCs) and mini IPSCs (mIPSCs) in which the slice is bathed in sodium channel blocker TTX would allow for observation at the single synapse level, which is not necessarily what is seen in spontaneous EPSCs, as presynaptic cells could potential form multiple synapses on a postsynaptic cell. After observing the changes in mEPSC and mIPSC amplitudes, the distribution of mushroom and filopodial spines for the given abGC can be analyzed and potentially make the above claim more persuasive.

In addition, it is strange that a p of 0.13 is enough to spur a distribution analysis, while a p of 0.17 for an increased spine density data of PLX-treated mice in 5C is not. To the reader, it appears that the researchers are choosing to continue statistical analysis on the dataset that best supports their hypothesis instead of the spine density data that doesn’t fit as well into the constructed narrative. Both distributions should be shown to prevent this impression.

Finally, the information presented in figure 2 supplement 2 and figure 3 supplement 3 should be moved into main figures. It is too important for understanding the central tenets of the paper to not be. Room could be created by reformatting some of the parts of Figures 1-3, particularly the heat maps in 2 and 3 and the cumulative probabilities of figure 1.

Minor Criticisms<br /> Although the paper presented a variety of great imaging techniques and results, there were several smaller errors and inconsistencies we noticed. It would have been very helpful to see a figure that summarizes all of the experiments. This would greatly improve the overall structure, as well as help guide the rest of the following figures. The paper would be significantly more organized. One criticism of note is the use of chemical structures and the lack of them ever being named. There is no reasoning for why these structures were included. They could have been labeled with their names or with a shortened abbreviation. If neither of those options were satisfactory, there could have been a table listing the names of the structures once at the very beginning in Table 1. This would have made the subsequent figures much more lucid. In Figure 1 D-G and I-L the use of cumulative probability is a questionable choice of visualization. In this case and context it does not work well. The raw data is usually fine to include, but, in this case, there are far too many data points which obscure the error bars. It would be better to see the error bars in order to visually see the significance.

In Figure 2, there were several issues. Figure 2D was very confusing. The set-up of using exclusively molecular structures without any names was a poor method of displaying the results. Figure 2H had poor axis choice for the rain cloud plot that made the figure unnecessarily confusing. 2E further added to the general confusion in Figure 2. Part E had an unclear heat map that was worsened due to the Y-axis orientation. This heat map is not ideal for expressing this data, since the goal was not to illustrate overall signal strength. It would have been better to see a quantification of how many dendrites were responsive above a certain threshold.

Figures 3 and 4 could also benefit from slight adjustments. The most glaring is the need for a median response amplitude to help form the comparative baseline for reproducible results; without this metric, it is difficult to make appropriate judgments. There should have been a median response amplitude between control and PLX like what was done for figure 2. This lack of consistency detracts from the paper.

In addition, in lines 112-114 it was stated that “granule cell odor representation is significantly different in awake mice”. This statement should have been elaborated upon to convey your thought process to the reader. This part is particularly indicative of a general issue with the writing; the paper is filled with references to previous explanations that are both lacking and not very coherent. Although referring to previous explanations is fine, this should not be used so heavily in this manner, as it seems apathetic to conveying the motivations and nuances of your experiments to the reader.

Lastly in Figure 6, changes should be made for the axis labels and scales. 6D and 6G should have the amplitude scaled to pA and not Hz for the Y axis. Figure 6 should also include labels for each group for the EPSC versus the IPSC.

Future Directions<br /> Collaboration between the Stevens group and the Murthy group has rich potential for understanding the integration of abGCs into the OB through the lens of microglia as key organizers. Many of the future directions we will present here take inspiration from recent work from the two groups and various articles cited in this paper and in their other recent papers.

The current paper investigates the impacts of microglia ablation on integrating abGCs into the OB on a long time scale, so the logical next step is to look at these changes on a shorter time scale. Wallace et al. 2017 tracked changes in responsiveness and morphology of abGCs integrating into the OB in real time, demonstrating a means for studying the role of microglia in this process.

As abGCs in the OB will eventually receive input from various populations of excitatory and inhibitory cells, it is conceivable that microglia play varying roles in the normal innervation by these populations. It would be interesting to look at the changes in morphology and stability of developing synapses and abGC response to these new inputs in the absence of microglia. Such experiments would complement work done by other groups in characterizing the timeline of abGC integration into the OB.

A defining feature of this paper and Wallace et al. 2017 is the presentation of a panel of odors which allowed the authors to study functional responses of abGCs. These results showed a high degree of heterogeneity in functional responses at both single time points and changes over the course of weeks. An experiment following abGCs through development could determine if the categories of dendrite-odor pairs’ responses occur in the same proportions in the absence of microglia. After tracking the response of single cells and identifying which category they fall into, a patch-seq protocol could be followed to identify electrophysiological and transcriptomic profiles that may correspond to these categories.

Continuing on the work done in Wallace et al. 2017, odor enrichment in the absence of microglia may prove interesting. If microglia ablation changes the stability of responses, the increase in response stability in mice with odor enriched environments may also be affected. To contrast the enrichment experiments, the authors could look at the role of microglia in mice with odor deprivation through naris occlusion. Additionally, as abGCs are more responsive to novel odors than pre-existing GCs, presenting microglia-ablated mice with an unfamiliar scent could also help define the role of microglia in guiding the integration of abGCs into existing OB circuits.

Behavioral and cognitive processing changes due to microglia ablation would be another potential direction. Although this paper showed changes in EPSCs, how does this change how the mouse processes olfactory information? Some kind of odor discrimination task would help determine if the ~20% decrease in EPSC amplitude causes any discernable change in the mouse’s discrimination ability.

After stopping PLX treatment, microglia are known to repopulate the entirety of the brain within 1 week. Given that maturation of abGCs takes roughly 3 weeks, reintroduction of microglia into the OB while abGCs are integrating into the circuit could be a very elucidating experiment. As acknowledged in the paper, it would be very beneficial to find a means for targeted administration of PLX to the OB to reduce any confounding effects of ablating microglia in the whole brain.

On 2019-12-16 15:04:49, user Carly Langan wrote:

NE598 Group 4 - Rhushikesh Phadke, Michael Melhem, Tony Lopez, and Carly Langan

The first author of this review is a graduate student at Boston University while the other three authors are senior undergraduate students at Boston University. This review was assigned as part of our Neural Circuits (NE598) course.

Microglia are necessary for normal functional development of adult-born neurons in the olfactory

Jenelle Wallace, Julia Lord, Lasse Dissing-Olesen, Beth Stevens, Venkatesh Murthy

Summary:<br /> Microglia are critically important for normal brain development and have been implicated in the regulation of synaptic development and activity dependent synaptic pruning. In addition to this, most of what is known about microglia is the implication of their role in both injury and disease. However, despite our growing understanding of these cells, very little is known of the role of microglia in adult neurogenesis. Since recent studies have shown increased activity in olfactory bulb neurons after microglial ablation, Wallace et al. investigated the mechanisms, both cellular and circuit related, behind this effect in order to determine the role of microglia in adult neurogenesis, specifically in adult born granule cells in the olfactory bulb of mice.

In Figure 1, the authors demonstrate how microglia preferentially interact with mushroom spines on developing adult born granule cells (abGCs). In vivo two-photon imaging showed overlap of microglia and abGC dendrites in the EPL of the OB. Using a time lapse of every 3 minutes, Wallace et al. demonstrated high motily of microglia with high proximity to dendritic spines. Interaction frequency generated from actual imaging data compared to offset pixel shifted data, showed how microglia preferentially interact with mushroom spines rather than filopodial spines. Microglia spent twice as much time interacting with mushroom spines in the actual data rather than the offset data, solely on the account of number of interactions. There was no significant difference between offset and actual data when it came to duration of interactions.

In Figure 2, Wallace et al. show that microglial ablation during development reduces odor-evoked responses of abGCs in anesthetized mice. Researchers depict the experimental timeline for microglial ablation during development of abGCs. Iba1 staining in the olfactory bulb of the control vs the PLX-treated mice showed efficient ablation of microglia. Microglial ablation did not impact the overall number of adult-born neurons in the OB. Monomolecular odor responses were analyzed by looking at calcium responses in abGC dendrites as well as simultaneously imaging morphology. This showed sparse abGC responses to odors but enough to identify dendrites responding to most of the odors. Average responses, number of effective odors, and lifetime sparseness were calculated for both controls and PLX-treated mice. Mean values of dendritic responses showed lower responsiveness to odors and to fewer odors in PLX-treated mice as well as a decreased lifetime sparseness.

In Figure 3, the authors repeat the panel of experiments in Fig 2, except this time they do so for awake mice to show that the state of the mouse does not affect the outcome. They found that the results turned out to be consistent, with dendrites in PLX treated mice showing lower responsiveness as well as responding to lower number of odors. The authors also went on to show that the active sniffing rates between awake and anesthetized mice were not significantly different to confirm that any baselines changes were not due to changes in active sampling of odors.

To test whether the effect of Microglia ablation was development specific, in Fig 4,the authors resorted to depletion of microglia post development of abGCs. PLX was given three months after lentiviral injections to ensure complete maturation. In this case, the authors found between control and PLX treated mice with respect to distribution of responses, lifetime sparseness or number of odors evoking a response.

In Figure 5, researchers used lentiviral labeling to visualize spine morphology in vivo of mushroom spines in the EPL that microglia were previously shown to preferentially interact with. Apical dendrites in abGCs between control and PLX-treated mice had no significant difference in spine density, but did have a distribution of spine head volume that was shifted toward smaller volumes in PLX-treated mice that did not reach significance when averaging across all spines in each cell.

In Figure 6, the authors analyzed the effect of microglial ablation on the abGCs during development. They injected lentiviral labels 3 weeks after mice were exposed to either PLX5622 or control chow and recorded after 5-6 weeks. They found that mice exposed to PLX5622 had decreased EPSP amplitude compared to control mice while the frequency was unchanged. They also show that IPSPs frequency and amplitude was unchanged.

In Figure 7, the authors used essentially the same technique as in figure 6 but instead observed abGCs after development. They inject mice with the lentivirus then expose them to PLX5622 or control show after 2 months. 3 weeks later, they record from slices of the mice brains. The authors found that EPSP amplitude and frequency was unchanged in abGCs after development.

Merits:<br /> Overall, this paper was extremely well written. Researchers found that microglia were necessary for the development of abGCs in the OB, and preferentially interacted with mushroom spines. Ablation of microglia during development lead to a reduction in odor responses, but did not do so in preexisting GCs. Microglia preferentially targeted mushroom spines on abGCs, and microglia ablation lead to a reduction in mushroom spines and an increase in smaller spines with weaker excitatory inputs. In addition to this, the paper advantageously used both in vivo techniques as well as two-photon microscopy. The use of a timeline in many of the figures was very beneficial to the reader, as it provided a better understanding of how the experiments took place.

Specific Critiques:<br /> In Figure 1, it would be useful for the study to also show a more of a time lapse to see more minute changes, as every 3 minutes seems fairly broad. It would also be interesting for the researchers to note why the specific time frame used was chosen. Having images taken three minutes apart, we may be missing out on certain interactions that are occuring.

In Figure 2 (D, F, H), it would be beneficial to the reader if a figure key was included so as to help the reader to better understand what the data is showing. Although the colors representing both control mice and PLX-treated mice are mentioned earlier in the figure, it would be helpful to keep the labeling present throughout the figure. Although Figure 2 references a total of 16 monomolecular odors tested, only a total of 15 are shown in Figure 2. If there was a monomolecular odor that was eliminated in the study, it would be helpful to mention that. In Figure 2, the results suggested that with the ablation of microglia in the developing abGCs, there was a reduction in odor responses, however, the microglial ablation itself was not entirely precise. Therefore, if other cerebral locations showed diminished microglia, this could have impacted the overall results achieved.

In Figure 3, it is claimed that the response is the same in awake mice as anesthetized mice. However, it is interesting to see that the significance level has considerably dropped in the cumulative distribution of responses, which may indicate a much lower response rate. Also, the claim that the number of effective odors as well as lifetime sparseness has changed is not supported by the p value which is above 0.05. This might be due to the fact that the sample size of these mice was slightly less that the anesthetized mice, so although the trend is there, significance is not reached.

In figure 4, an important point that might affect the experiments is the difference between cranial window implantation time and lentiviral injection in comparison to Fig 2. In Fig 2, the implantation is done at -6 weeks but here it is -3 weeks. Because implantation can lead to microglial activity, it would be good to maintain consistency in the timeline of similar surgeries.

In figure 5, we believe that similar sample sizes should be used for both groups. There were roughly 2 times as many PLX mice spines used in the study compared to control mice. Another concern is the use of two different statistical analysis tests. The Wilcoxon rank sum test was used in both statistically insignificant comparisons while the Kolmogorov-Smirnov test was used in the lone statistically significant comparison.

In figure 6, it would be beneficial to analyze the effect microglia ablation might have on mini EPSPs (mEPSP) or mini IPSPs (mIPSP) versus sEPSP and sIPSP. As opposed to sEPSPs or sIPSPs, mEPSPs or mIPSPs block the cells ability to have action potentials so the resting cell voltage can be measured without action potentials.

In figure 7, we recommend including an analysis of IPSPs to maintain consistency with figure 6. Although the IPSPs were unaffected in developing abGCs, they might be altered in abGCs after development.

Minor Concerns:<br /> One thing that would have made this paper even stronger, would have been to better discuss the motivation for this study. Though the authors begin to talk about their motivation for understanding the role of microglia in adult neurogenesis, they do not mention why this kind of information would be beneficial for the scientific community.

In addition, cumulative probability maps are not great when trying to tell when something is statistically significant. They provide a poor visualization and the raincloud plots researchers used previously to illustrate differences between control and PLX-treated mice were much nicer for the reader and provided an easy way to view any differences.

It would be beneficial for the researchers to include what each of their experimental results suggest for the big picture of this research study. This was done for the analysis of Figure 1, but would be helpful to readers if it was done after each of the experiments conducted.

Amongst another minor concern is the fact that the number of odors in the panel has been varied between 15 and 16, which might just be a typing error. If not, then an explanation for this would be helpful. Along with this, consistency in representing statistical data, such as showing or not showing the p value each time would make for better presentation and would enable readers to assess all figures similarly.

In terms of minor grammatical issues, make sure to be referencing the proper figure when discussing it. We found a few instances of improper references, (In Line 168 referencing amplitude of recorded events should be Fig 6G, not Fig 6F),(In Line 118, referencing lower lifetime sparseness should be Figure 3D, not Figure 3C),(In Line 159, referencing reduced amplitude should be Figure 6D, not Figure 6C).

Future Directions:<br /> Overall, there were many different experimental parameters that were covered throughout the paper. There were, however, a few thoughts we had on what may be beneficial for future experiments. One of these suggestions would be to instead use CRISPR or a Cre line to target the ablation to certain cells, and therefore be able to do a conditional KO. This would allow cellular ablation to be more localized than with the PLX chow, and would give the researchers more control over the areas they were observing. Beyond this, ablation with PLX5622 is not completely effective and there may be remaining amounts of microglia.

Another suggestion we thought of may be to provide more insight into what occurs behaviorally with the ablation of microglia in the OB. Although in vivo calcium imaging is difficult to perform, the results that it may give to this research study would be beneficial overall. Specifically, it would be interesting to have in vivo two photon calcium imaging performed while there was an odor discrimination task being performed.

Besides using different methods to ablate specific subgroups of microglia, there are two regions within the brain known for neurogenesis, the olfactory bulb and the dentate gyrus. Although previous research has shown that microglia regulate adult neurogenesis in the subgranular zone of the DG through phagocytosis of apoptotic neuroblasts, researchers could view whether there is any overlapping function with regards to how microglia target spines on abGCs to promote excitatory synapses. Previous research shows possible conserved evolutionary patterns within the brain, it’s possible this form of microglial interaction could be conserved between regions that feature similar phenomena.

On 2023-12-15 09:32:38, user NeuroLab@CU wrote:

Introduction, 1st paragraph: Part of the controversy might be explained by disagreement of what reorganization means and that structural and functional reorganization are two types of reorganization. Comparison two microelectrode mapping experiments might also reveal that BOLD and neural electrical activity (spikes) are not directly comparable and might give different mappings. How else to explain the findings by Merzenich et al. or that 1 year after amputation in monkeys nearly whole BA3b responds to the stump (Manger et al., 1996)?

Intro, line 81: "Decisive is a big word with N=2 which are patients with a long disease history and very special treatment after amputation! How might disease and treatment have contributed to altered representation before or maintained representation after amputation, respectively? This should be discussed in light of studies showing that specific prostheses, treatment including usage of the (phantom) hand, stimulation of adjacent body parts, etc. can rverse maladaptation and phantom limb pain.

Results: I miss results from the toe mapping!

Discussion, line 400f: I think there is consensus among most researchers that functional reorganization not necessarily requires underlying structural reorganization.

Lines 405ff: It should be discussed that BOLD mapping probably is not equivalent to microelectrode mapping (Disbrow et al., 2000; Huettel et al., 2004). You argue that top-down modulation might underly changed functional representations. Couldn't the ssame argument be applied here in that top-down or connections from the motor cortex during (imagined) movements modulate BA3b activation?

Line 411f: I would say, it suggest the preservation of the structural connections determining the space for functional remapping. From where if not the motor cortex (or other top-down) shall the signal to SI come without tactile stimulation? Proof could come through stimulation of topographically corresponding neurons/nerves in spinal cord or thalamus (specific afferents).

Line 423: "functional integrity" -- should it not read "structural integrity"?

Lines 433ff: But that is exactly how (functional) reorganization and phantom sensations have been proposed to be treated - to maintain meaningful input! Therefore, these two patients are explicitly not the decisive sample to deny reorganization after amputation. Also, Shone et al. (2021; Cognitive Neurology) stated: "While today it is agreed that there is no simple relationship between somatosensory map reorganisation and PLP, the maladaptive plasticity theory dominated contemporary research on PLP for several decades. This theory provided clear predictions on how to treat phantom pain: if pain is caused by maladaptive reorganisation, then we need to reverse it to alleviate phantom pain. In that vein, several treatment approaches attempt to restore the representation of the missing hand by increasing the phantom hand’s motor control..."

Methods, Line 682f: Is it the M1 activity that is stabilizing the S1 functional reporesentation? Or is it top-down activation? The authors from (57) state: "...important to also consider motor contributions to digit representation". And they also show very different mappings for active and passive stimulation.

Lines 690ff: Why do both paradigms result in similar maps? Is it not touch that is represented in BA3b? Or is it overwritten with information from motor cortex?

On 2021-02-26 01:58:25, user Zachariah Cross wrote:

Hi Moritz,<br /> No, we used the functions implemented in MATLAB from Wen and Liu (2015; https://purr.purdue.edu/pub... "https://purr.purdue.edu/publications/1987/1)"). I have, however, been using the YASA implementation of IRASA on resting-state EEG datasets and have been getting good exponents estimates (i.e., they are comparable to exisiting literature, exhibit relationships with cognition/behaviour that are analogous to what has been reported). Perhaps contact the YASA developer, Raphael Vallat, to discuss your concerns? He's been extremely helpful for me in the past.

On 2025-04-01 13:27:43, user Katelyn Begany wrote:

Are the supplementary tables available at this time? I'm looking for Table S2. Thank you!

On 2018-08-28 04:45:45, user Leon French wrote:

This is a helpful and detailed guide. I have one small note that would add to the guidance for integrating the data -

I suggest that the authors mention on page 10 for step 4 that the Allen Atlas ontology classifies the hippocampus as part of the cerebral cortex (also the piriform cortex). While most neuroimaging tools do not. The gene expression profiles are very different between the allocortex and neocortex. Mistakenly using the hippocampal expression profiles when integrating information from the cortical sheet may give misleading results.

On 2019-10-03 12:46:50, user Bryan Ward wrote:

It might be worth considering the effects of the 7T magnet on the vestibular system. The exponential decay in learning error is similar to the adaptation of spontaneous nystagmus in the 7T MRI and vestibular stimulation projects to areas involved in spatial localization. <br /> Bryan <br /> Roberts, Dale C., et al. "MRI magnetic field stimulates rotational sensors of the brain." Current Biology 21.19 (2011): 1635-1640.<br /> Jareonsettasin, Prem, et al. "Multiple time courses of vestibular set-point adaptation revealed by sustained magnetic field stimulation of the labyrinth." Current Biology 26.10 (2016): 1359-1366.

On 2017-08-22 03:33:38, user Camilo Libedinsky wrote:

Also, in addition to seeing a slight decrease in the number of pure selective cells after learning, did you observe a decrease in the information they carry? I ask because in our data (unpublished) we observed lower decoding accuracy when comparing pure with non-linear mixed selective cells. It is not directly related to my question, but a decrease after learning could explain this observation in our experimental data.

On 2016-02-21 06:07:40, user Yaroslav O. Halchenko wrote:

Well -- although it will sound like I am just trying to squeeze a citation in (http://www.gigasciencejourn... "http://www.gigasciencejournal.com/content/4/1/31)") but I guess I wrote that piece for a reason.<br /> You state that pillars of "Open" science are data, code and papers... but oh well -- none of those alone make it "Open". Even deposition of all those online and publishing in "open access" journals doesn't make them sustainably open, since those might (and do) go away in X years, and if noone had permissions to duplicate, reuse, improve upon your works -- what "open" is that? It is as open as a door of a limo standing at your doorstep but which you cannot ride.<br /> To guarantee that science product is open, it must be allowed for its widest dissemination and reuse. For that clear statement of copyright and license terms must be made, and no "exclusive licenses" be provided to take away those freedoms and place them into a single hand (which is often the case with publications in some "open access" journals). But this manuscript doesn't even mention a word "license". IMHO it is ignoring an elephant in the room ;)

On 2019-11-07 20:14:28, user Robert Sachdev wrote:

This paper is now published in the Journal of Neuroscience. https://www.jneurosci.org/c...

On 2022-03-25 10:19:30, user meditate not dissociate wrote:

This study does not describe the tested population characteristics, number of participants or exclusion criteria used (as is the case with previous published research of Sudarshan Kriya). Nine published studies of adverse effects over 15 years are not acknowledged or referenced here. These omitted studies are known to the author's institution 'Sri Sri Institute for Advanced Research' and are cited in and form the basis of their partner organisation's Art of Living online Health Policy for paid participants to learn their trademarked Sudarshan Kriya. Adverse effects are described there by Clinical Psychiatrists Dr Gerbarg and Dr Brown after observing 200+ people after 2004 tsunami and describe as contraindications the following; PTSD with significant dissociative symptoms, Dissociative Identity Disorder, bipolar disorder, schizophrenia, seizure disorders, psychosis. Previous published research describing the health benefits of Sudarshan Kriya further omit the 9 published studies which describe these adverse effects however typically employ a variety of exclusion criteria for the purpose of their research which are based on the adverse effects described in those omitted studies.

On 2020-08-23 08:39:16, user Sonia Tangaro wrote:

Interesting work! We also studied brain age in both TD and ASD by using different algorithms (Support Vector Regression, Random Forests, Lasso Regression) . <br /> In Brain Sciences 10 (6), 364, 2020 we also evaluated the most predictive features for age prediction in both populations https://www.mdpi.com/2076-3...

On 2019-07-19 10:00:06, user JJ wrote:

I wonder why you imply that this is a pure implicit process. You actually did not measure that in any meaningful way. Off course, you instructed your participant not to do that but what is the proof that they actually did not use a strategy.<br /> Also, you should not confound awareness and implicit. One can be unaware of the perturbation and still choose to aim in another direction. Similarly, awareness is only measured at the end of the experiment. At that time, participants could have forgotten about what happened 80 trials ago. IMO, there is no real proof that what you studied here is purely implicit unless you actually try to measure it.

On 2018-07-03 12:00:31, user Abraham Nunes wrote:

Hi Taylor,

My sincerest thanks for your thoughtful questions <br /> and your annotations (which I cannot seem to see; link appears broken). I<br /> will address your points in sequence here.

1. I did no preprocessing on the activation foci beyond what was already done in the neurosynth package. When you return the activations for from their <br /> base data object, you have the exact representation I used for the foci.

2. At face value, I don't see why that wouldn't work.

3a. Great points! First, the word embedding should consolidate synonyms, so<br /> there should be no reason to do that. In fact, I already preprocessed <br /> the text more than most of my neural-network-based NLP colleagues would.<br /> One interesting thing I've found in my experience with these embeddings<br /> is that tracking the loss function is relatively useless for gauging <br /> when to stop training. I actually monitor a large and broad set of terms<br /> and stop training when the semantics are reasonable. This is a <br /> limitation of embedding algorithms, in my view, but shows why synonyms should not be reconciled as a preprocessing step.

3b. I have been collaborating with a colleague for the next iteration of this paper <br /> (should be out soon; just finishing the manuscript update and code repository) in which we used the MeSH database to identify multi-word phrases (e.g. functional\_magnetic\_resonance\_imaging) which were included in the embedding. There are also a couple of other new analyses in there.

1. When I released the initial version of this paper, I did so quite rapidly and the code was a bit idiosyncratic (I tend to write very short code that looks a bit more <br /> mathematical and so there are few comments.). For the next release---in <br /> which we have a couple of additional analyses---we will be simultaneously releasing the code on GitHub. Consequently, we're going through it to make it readable for everyone as we speak. The embeddings and word2brain() function should all be usable, too.

Thanks again for taking the time to go through our paper. Further suggestions are always welcome.

If you wouldn't mind, I'd love to see those annotations. My contact information can be found on the header bar at www.abrahamnunes.com.

Cheers,

Abraham

On 2023-08-15 18:16:12, user Charles Warden wrote:

While there are some changes, I believe this is the peer-reviewed version of the article:

https://pubmed.ncbi.nlm.nih...

I very much appreciate being able to work with the lab for this study!

On 2018-04-15 08:36:46, user Andrey Palyanov wrote:

There is a misprint at the 3rd line of the Introduction (at 1st page) - it is written that a human brain comprises 98 billion of neurons, while it should be 86 billions (according to Azevedo FA, Carvalho LR, Grinberg LT, Farfel JM, Ferretti RE, Leite RE, Jacob Filho W, Lent R,<br /> Herculano-Houzel S. Equal numbers of neuronal and nonneuronal cells make the human<br /> brain an isometrically scaled-up primate brain. J Comp Neurol. 2009). It is quite hard to reload pdf of the manuscript, that's why I just mention it here.

On 2023-05-25 18:47:14, user Rich Krolewski wrote:

Published as: https://www.nature.com/arti...

On 2019-11-05 02:01:54, user michele assef wrote:

We are students in a Neural Circuits course at Boston University, reviewing this paper for an assignment:

Summary:

Multiple sclerosis (MS) is an immune-mediated, demyelinating neurodegenerative disease of the CNS that involves both white and grey matter degeneration. The white matter pathology of MS is known to involve the complement system, a part of the immune system comprised of three pathways and a large number of plasma proteins that opsonize pathogens. Excessive CNS synapses are eliminated during development to establish mature patterns of neuronal connectivity. The binding of antigen-antibody complexes to the C1q protein initiates the classical complement pathway, which, in turn, activates the complement system.

The classical pathway converges with the alternative and lectin activating pathways to cleave C3 to C3a, a chemokine that recruits phagocytic cells, and C3b, which covalently binds to the surface of invading pathogens and acts as an opsonin, facilitating phagocytosis via the microglia-specific complement receptor 3 (CR3). The cleavage of C3 via one of these three pathways leads to the formation of C3 convertase, or C4bC2b complex (C4b2b3b). The binding of C3b to C4b2b3b ultimately results in the formation of a membrane attack complex (MAC) that causes cell lysis. The early events of complement activation are implicated in the synaptic pruning observed in MS patients.

While the role of complement in MS white matter pathology has been well-established, its role in grey matter degeneration has only begun to be addressed. Thus, Hammond et al. sought to establish a causal relationship between C1q/ C3 activity and MS grey matter pathology. The existence of such a relationship may allow researchers to better understand and hopefully treat MS. Hammond et al. created MOG35-55 EAE mice that had been immunologically modified to produce MS-like symptoms. To determine whether C1q and C3 are involved in synapse elimination in EAE hippocampus, the authors performed Western Blotting of sham and EAE mice hippocampi for C1q and C3 (Fig. 1A). They found that C1q and C3 protein and mRNA levels were significantly elevated in EAE mice hippocampi (Fig. 1B, C). The same trend was observed in microglia: both C1q and C3 mRNA levels were elevated in EAE microglia/myeloid cells, but this was only significant for C3 mRNA (Fig. 1D). This observation indicates that EAE mice have higher levels of C1q and C3 in the hippocampus, and that C1q and C3 might play a role in MS neuropathology.

Hammond et al. performed immunostaining of sham and EAE hippocampi, discovering that C1q levels were elevated in EAE hippocampi and were much higher across all EAE hippocampal sub-regions (Fig. 2A-C). The authors also found higher levels of C1q around hippocampal blood vessels in EAE mice (Fig. 2G). C1q were found at higher densities throughout hippocampal synapses in EAE mice (Fig. 2E), and a similar observation was made with C3 (Fig. 2I, 2J). Overall, it was noted that C1q and C3 levels were elevated in EAE mice brain, implicating C1q and C3 as potential proteins involved in MS neuropathology.

Comparisons between WT, C1qaKO, and C3KO EAE mice clinical motor scores relevated that scores were significantly lower for C3KO EAE mice than WT or C1qaKO mice, while there was little change between WT and C1qaKO EAE scores (Fig. 3). Thus, the authors claim that C3, but not C1q, is primarily responsible for MS pathology and motor symptoms. Furthermore, Hammond et al. compared immunostaining of Homer1 and PSD95 between sham and EAE WT, C1qaKO, and C3KO mice (Fig. 4A-B) and noted reduced synapse loss in C1qaKO and C3KO EAE mice (Fig. 4C-D), demonstrating that C1q and C3 are involved in MS synapse loss based on the EAE mouse model.

Finally, the authors stained brain sections for IBA1, which signifies microglial activation (Fig. 5A), and found that there was less staining in C3KO EAE mice brains compared to WT EAE brains, while there was no significant difference between C1qaKO EAE mice and WT EAE mice brains (Fig. 5B-E). This indicates that C3, but not C1q, is specifically involved in microglia activation, demonstrating a potential mechanism between C3 and microglia in MS neuropathology.

In summary, Hammond et al. sought to understand the relationship between C1q and C3 in MS grey matter pathology and found that C1q and C3 are indeed elevated in an EAE mouse model of MS and that C3, specifically, is involved in motor deficits and synapse loss in EAE.

Merits:

The authors make a good effort to strategically set up their research question and intentions for the study toward the end of the introduction. It is clear that they are seeking to determine whether there is causality between the complement pathway and neurodegeneration in animals, and therefore potential for targeting with drug therapy in the future.

Specific Critiques:

The authors quantitate both protein and mRNA expression levels of C1 and C3 in sham vs. EAE conditions. Figure 1D examines microglia and myeloid mRNA expression levels, but does not look at protein expression levels of C1 or C3 in microglia or myeloid cells. Due to the drastic difference between C1 and C3 protein and mRNA levels it would be beneficial to measure the protein levels in microglia/ myeloid cells as well.

The authors make use of genetically modified animals in the form of C1q and C3 knockout, KO, mice. As seen in Figure 2, IHC was performed on both WT and C1qa KO mice to demonstrate the animal in fact lacked the protein. The authors fail to perform a similar experiment for the C3 KO model they use. A western blot between WT and C3 animal would have been an important proof of concept demonstrating their C3 KO model is in fact, a knockout.

Hammon et al. use C3 and C1qa KO animals to demonstrate that their ablation from the complement system is effective on reducing EAE symptoms. In Figure 4, Homer1 positive puncta are quantified between sham and EAE mice for all three conditions WT, C1qa, and C3. While comparisons are made between sham and EAE conditions, no comparisons are made between the different genotypes, i.e. WT sham is not compared to C3 KO sham or C1qa KO sham. These differences are important to note as they may be inherent differences of Homer1 positive puncta between genotype regardless of sham or EAE conditions.

Figures 1 and 2 focus on the hippocampus as the region of interest for C1 and C3 expression, but Figure 3 then examines the effect C1 and C3 KO has on motor deficits. There is a bit of a disconnect between the hippocampus and motor behavior. The paper would have been strengthened by either examining motor cortex in Figures 1 and 2, or exploring what behavioral and or memory deficits are observed in relation to the hippocampus data.

Minor Critiques:

It is noted that the age of the mice is between P25-30 once in the paper, but it would be easier on the reader if the age of the mice at the time of the experiments were placed in the figure captions. This may reduce unnecessary movement between pages.

When speaking about the statistics performed during their experiments the authors frequently refer to “student’s t-test”. The way in which this was written is inconsistent throughout the paper. It is primarily noted as “t-test”, but is occasionally written as “t test”. The way in which a statistical test is written should remain correct constant throughout the paper, in this case it should be written as “Student’s t-test”.

On page four, in the introduction, the sentence “Thus, the goal of this study was to evaluate whether complement-dependent synapse loss contributes to grey matter degeneration in EAE, which may also provide insight into its role in MS..” contains two periods at the end, one should be removed.

On page 18, in the Discussion, the authors state, “This is the first study to investigate complement’s contribution to synaptic damage within EAE grey matter…”. It would be beneficial to remove the portion of the sentence stating that this is “the first study”. The timing of the study is not as relevant as the impact on the field. Those familiar with the literature will be aware and appreciative of this being a novel investigation.

Future Directions:<br /> This paper examines how excitatory synapses are affected in EAE, and it would be valuable to examine inhibitory synapses as well. By conducting similar experiments, the authors would be able to determine both if inhibitory synapses are affected in a similar manner as excitatory synapses in EAE, and if so, whether or not the removal of C1 or C3 from the complement system attenuates the effects of EAE to the same degree in inhibitory synapses.

It would be valuable to examine the cognitive effects that EAE has. EAE is often used as a model to emulate MS, and because MS does not only cause motor deficits, examining the cognitive effects are just as important. Some cognitive symptoms caused by MS include depression, anxiety, and mood swings. These behavioral changes can be observed in EAE mice through experimental paradigms such as the three chamber novel stranger mouse and grooming behavior.

Observing neurodegeneration in brain regions other than the hippocampus would be an interesting follow up to this study. Regions important in the maintenance of other neural circuits which are affected in MS can be investigated. For example, looking at mPFC as this region is important in cognition and self referential processin

On 2019-11-04 20:22:46, user Michael Melhem wrote:

The first author of this review is a graduate student at Boston University while the other three authors are senior undergraduate students at Boston University. This review was assigned as part of our Neural Circuits (NE598) course.

NE598 Group 4 - Rhushikesh Phadke, Michael Melhem, Tony Lopez, and Carly Langan

Complement-dependent synapse loss and microgliosis in a mouse model of multiple sclerosis<br /> Jennetta W. Hammond, Matthew J. Bellizzi, Caroline Ware, Wen Q. Qiu, Priyanka Saminathan, Herman Li, Shaopeiwen Luo, Yuanhao Li, and Harris A. Gelbard

Summary:<br /> Since multiple sclerosis involves microglial activation and a reduction in synaptic density, Hammond et. al used experimental autoimmune encephalomyelitis (EAE) to model these key features in the grey matter pathology of mice. The complement system, a set of proteins shown to upregulate immune responses, participates in opsonization of myelin and debris, and has been implicated in white matter pathology in MS patients. This system is initiated through the deposition of C1q, leading to a signalling cascade that cleaves protein C3 into sub subtructures. The goal of this paper sought to determine, through analysis of C1q and C3 protein levels, whether or not complement-dependent synapse loss contributed to the degeneration of grey matter in EAE.

In Figure 1, the authors provide a basic characterization of protein and mRNA levels in the hippocampus using both Western Blotting Techniques and data quantification. Western blot images show expression of both C1q and C3 proteins in the Sham and EAE mice with a stronger signal of both C1q and C3 in the EAE mice. In addition to this, C1qa and C3 with fold changes in mRNA expression were analyzed by qPCR. C1qa and C3 mRNA levels were found to be greater in hippocampus of EAE mice than in the sham control mice.

In Figure 2, researchers immunolabel C1q in EAE and WT, and quantify fluorescence across various parts of the HPC, showing increased overall C1q expression in EAE mice. IHC images show that C1q puncta somewhat overlap PSD95 puncta and that C3 expression is increased in CA1-SR. Researchers attempt to show where C3 expression is localized, finding it around blood vessels in sham and EAE mice. Lastly, researchers show occasional overlap between C3 puncta and PSD95.

In Figure 3, the authors depict the mean clinical scores of motor deficits from EAE immunized WT, C1qa KO, and C3 KO mice 0-26 days post immunization. C3 KO mice showed a significant decrease in the mean clinical score of motor deficits both 14-15 days post immunization and during the chronic phase, which was 20-30 days post immunization. When C1qa KO was tested, there was no change in the EAE disease course when compared with WT EAE mice. Both the C1qa and the C3 KO did not shift the timing of motor symptom onset. Overall, this figure demonstrated how the deletion of C3, but not C1qa, reduced the average EAE motor deficits.

In Figure 4, synaptic density was measured using Homer1 and PSD95 antibodies to tag and fluoresce synapses in CA1-SR cells of the hippocampus in SHAM and EAE mice. This figure showed that C1qa and C3 KO mice have less synaptic loss than EAE mice compared to WT mice. This change was not due to developmental change since the number of synapses was unchanged when comparing KO and WT mice.

In Figure 5, the authors looked at microglia activation induced by the EAE model. For this reason, they used antibodies against Iba1. Compared to SHAM injections, WT animals showed an increase in Iba1 intensity on EAE injections. This increase was seen in C1q KO but was absent in C3 KO. Along with an increase in Iba1 intensity, WT and C1q KO showed a decrease in skeletal length/volume, indicative of a change in morphology. This change was again absent in C3 KO mice.

Merits:<br /> Overall, this paper effectively demonstrates that the EAE model produced levels of both C1q and C3 that were significantly upregulated in the HPC. This trend was seen in both mRNA and proteins, but microglia contributed to C3 overexpression only. The increase in C1q was seen in many regions of the HPC (SR, SO, etc.) and both C1q and C3 overlapped with postsynaptic markers. Researchers showed the EAE model captured motor deficits found in MS as well as a progression in the EAE model that tends to affect motor deficits over time.

Regarding inflammation, researchers determined that the EAE model displayed increased Iba1 signals and indicated a difference in microglia morphology between SHAM and EAE mice. Finally, C3 was shown to be important for the level of Iba1 expression in microglia, while C1q had no such effects.

Specific Critiques:<br /> Overall, we felt that the sample sizes were low across all experiments and figures. The difference in sample size in Figure 1 between Wild Type and C3 KO may introduce an imbalance in the statistics of the study, and should thus be avoided. This can be done by increasing the sample size of other groups to match the wild type conditions. We felt as though the statement “no significant main effects of sex or significant interactions of sex with immunization status,”(pg. 6) was not able to be supported due to the smaller sample size used, and should therefore be emitted or supported with further data. It would be beneficial to see if all the mice administered with the EAE treatment show the same response or rather, if a subset of the population show it, therefore, outliers that show extreme results would not skew that data. Along with that, a baseline inflammation level for the KO would be helpful to see the inherent changes that occur when knocking out the complement genes.

In Figure 2, there was no quantification in figures D, E, I, or J. Without quantification, we’re left with subjective, unanalyzed images. As for the images used, Fig. 2E, I - J, which show putative overlap of synapses and complement proteins, are at such a low magnification that we are unable to properly see the morphology surrounding the synapses themselves. Other techniques, such as electron microscopy or super resolution microscopy (STED or SIM), could be used to help distinguish structures at a finer level. The figures which detailed synapses would be much more convincing if they were stained for cytoarchitecture. To prove that what we are analyzing is indeed a synapse, the use of both pre and postsynaptic markers, followed by co-localization studies, are recommended. In Fig 2G, we cannot definitively say that there is blood vessel colocalization with the complement proteins. This is true because there were no markers for blood vessels to show the C1q overlap.

In Figure 3, the sample size discrepancy can cause a major imbalance in data and may weigh it towards the control samples. Results from C3 KO mice (n=7), will not be as consistent or replicable as results from WT EAE mice (n=24). It would benefit the data greatly to use a consistent sample size across conditions. All the data shown here is only comparing EAE injected mice. It would be helpful to see a comparison made with SHAM mice to show the progression of the model throughout the age span referenced. Without that, a confirmed clinical score deficit cannot be claimed. In supplemental information, a video depicting the motor deficits examined would be useful in understanding the model better. This research study concluded a reduction in synaptic density in the hippocampus of EAE models, yet failed to demonstrate any cognitive or behavioral consequences of this loss. The inclusion of a change in cognition or behavior timescale would help to demonstrate some of the other deficits associated with MS. To compare the disease progression to complement protein expression, levels of C1q and C3 at different time points (such as P6, P18 and P28) would clearly relate the motor deficits to complement protein levels.

In Figure 4, it would be helpful to include more time points (such as P6, P18 and P28) which are critical to motor deficit progression as shown in Fig 3. Instead of just synaptic density, analysis of puncta size and shape of cells in SP, SO and SR would be beneficial. Since mean motor deficit scores have been shown to change in Fig 3, areas related to motor control, such as the spinal cord and motor cortex, would be good places to look for spine density. This is because they can directly correlate this to the disease progression in the model used. As suggested for Fig 2, a demonstration of cytoarchitecture with use of DiI crystals would make the image more comprehensible. Use of other markers, such as PSD95 (indicator of excitatory post-synapses), staining for synapsin (indicator of pre-synapse) and gephyrin (indicator of inhibitory post-synapses) would add more dimension to the study by distinguishing between the types of synapses pruned.

In Figure 5, only Iba1 was used to assess microglia activation. Iba1 has been linked to other physiological processes as well, but it cannot be considered the definite measure of microglia activation. Instead, other markers, such as CD68 and P2ry12 should be used to show phagocytosis. Use of different markers to analyze microglial states will give a more comprehensive measure of activation. The morphological change in Fig 5 D,E can be better represented by using Sholl Analysis. This would demonstrate dendrite intersections around the cell body. As a compact morphology has been shown to be indicative of activated microglia, this method gives an immediate representation of the state.

Minor Concerns:<br /> Regarding the writing of the paper, avoid the use of statements regarding the novelty of the experiment and review the paper for grammatical and syntactic errors (ex. “By western blot, found that…”, “Next,we”, “...provide insight into its role in MS..”).

When showing data in bar graphs, consider using the absolute value of data instead of normalized data for comparison between each condition. Also, please include the age of mice in all of the figure legends to assist the reader in understanding what time frame the data was extracted from.

In Figure 2, use insets for zoomed figures for ease of understanding. Here it would also be useful to pixel shift panels E and G to confirm co-localization.

Future Directions:<br /> Overall, the manuscript does not provide an explanation for if microglia are beneficial or harmful in the case of MS. For this purpose, it would benefit the study to target microglia in development (eg. using clodronate) and then analyze clinical scores over the same period of time. This data would be a good indication of the role of microglia in MS.

Furthermore, expand the scope of the experiment beyond the HPC. It might help to look at whether the PFC or AC (regions possibly implicated in MS) show similar microglial activation levels or possible synaptic loss.

The genes required for complement protein expression may be involved in developmental progression and, therefore, may affect regular synaptic density in a way not mediated by microglial activation. Does complement protein knockdown, potentially through the use of ASOs, result in the same affect? This would have important implications for MS treatments, as adult humans cannot have gene KOs, but could theoretically have ASO treatment.

Future studies should explore the cognitive deficits in the EAE mice during the time span post immunization. Multiple sclerosis is a disease characterized by more than just motor deficits, so seeing the effects of the EAE model in other aspects of the disease would be helpful. Some of the characteristics of this disease include behavior and cognitive changes. Considering that this study focused on the hippocampus, which is responsible for tasks related to memory and cognition, the demonstrated reduction in synaptic density would be expected to induce some type of cognitive effect. Behavioral changes could be analyzed by introducing the EAE mice to novel objects. Cognitive changes could be tracked over the time period by analyzing place cells in the hippocampus as the mouse runs through a maze.

To characterise spine density, whole cell patch clamping could be used as an addition to the density data. Frequency and amplitude of both mEPSCs and mIPSCs will be indicative of spine loss and any receptor changes that might be happening.

On 2021-11-29 18:27:02, user William Matchin wrote:

The review and discussion of the aphasia literature in this study is shallow and inaccurate. On page 41 it is asserted that "neither agrammatic production not comprehension have been consistently linked to damage to a particular brain region within the language network...". However, as reviewed in Matchin & Hickok (2020), frontal regions are *very* consistently implicated in agrammatic production, across populations (stroke, PPA) and methodologies (clinical impressions, quantitative assessments). See e.g. Wilson et al. (2010 - Brain), den Ouden et al. (2019 - HBM), Matchin et al. (2020 - NoL), and references cited therein. In addition, agrammatic comprehension has also been robustly and consisted associated with temporal lobe damage, and not frontal lobe damage (again, see Matchin & Hickok 2020 for review and references).

On 2022-04-02 13:31:37, user Edward Mao wrote:

For the first time, the authors has done a comparison between soft and hard subjects... great!

On 2025-09-30 19:26:55, user Greg Macleod wrote:

Fascinating concept! Vesicles (presumably glutamatergic synaptic vesicles) providing the new plasma membrane for LTP enlarged presynaptic boutons.

On 2020-08-10 20:48:23, user Muhammad Subhan Zahid Nazir wrote:

How come no one from the field of Psychology is a part of their team? I'd love to hop on board if given the chance.

On 2017-08-14 12:32:41, user Sebastian James wrote:

Alex, I don't think you've stated what the dopamine value was for the results in this paper. If the value in my copy of the code is right, then DOPAMINE (referred to as d on page 8 of the manuscript) was 0.2.

On 2020-01-21 18:34:08, user Scott B Hansen wrote:

Is it possible that terpenes are disrupting the membrane and activating TREK-1 indirectly through phospholipase D? This is how mechanical force and inhaled anesthetic activate TREK-1 and to some degree inhibition by local anesthetics.

https://www.biorxiv.org/con...<br /> https://www.biorxiv.org/con...<br /> https://www.ncbi.nlm.nih.go...<br /> https://www.ncbi.nlm.nih.go...

On 2022-09-01 06:33:58, user Alessandro Morelli wrote:

Certainly interesting, but can this active beta oxidation by<br /> myelin be explained by the active presence of mitochondrial components with<br /> morphologically identifiable mitochondria absent, as my group has shown? See<br /> article “Efficient extra-mitochondrial aerobic ATP synthesis in neuronal membrane<br /> systems” https://pubmed.ncbi.nlm.nih...

On 2019-09-05 23:31:23, user Justin Perry wrote:

Very interesting paper. I wonder how this work relates to the Kramer lab paper that came out earlier this year in drosophila, showing glia regulation of neuronal activity via Ncc69 (NKCC1 ortholog) and the upstream kinases (eg WNK). https://www.nature.com/arti...

On 2016-10-06 10:43:48, user Tom Wallis wrote:

I have posted replies to the open review we received on this article (see previous version comments) here: https://github.com/tomwalli...

On 2019-09-07 15:39:37, user Ricardo Mesquita wrote:

Great work!

I have sent some comments via e-mail to the corresponding author.

Ricardo

On 2023-06-30 23:44:13, user Tiago Lubiana wrote:

I have some comments, however, on the phrasing of your definition of a cell type.

The phrase I am concerned about is "We propose that a cell type should be robust to inter-individual variation, and therefore defined as a group of cells that are more similar to cells in a different brain than to any other cell in the same brain."

There is also a very similar variation to it in Fig. 6 that shares the same difficulties:

"Cell type is defined as a group of neurons which is more similar to a group in another brain than to anything in the same brain"

It is repeated with variations once more further in the text with the same problems:

"To address these complex typing issues we employ a new definition of cell type that<br /> uses inter-animal variability to determine when cell types should be split: A cell type is a<br /> group of neurons that is more similar to a group of neurons in another brain than to any other<br /> neuron in the same brain"

1. It is also not clear if you are stating how "cell type", the idea, should be defined, or how different cell types should be delimited.

Are you defining the concept of "cell type"? Or are you suggesting a methodology for identifying cells of the same type in practice?

A subtle, but very important distinction.

1. It is also important to notice that this is not a general definition of "cell type", as it is scoped to Drosophila melanogaster brain connectomes. This is obvious from context but needs to be explicit for it to be a precise definition. .

A definition that says "A cell type is a group of neurons" excludes all non-neuronal cells and is incompatible with the use of "cell type" for all other non-neuronal cells.

You would be more precise by saying "A neuronal cell type is a group of neurons". <br /> Also, not all neurons are in brains, so the definition also needs to make that clear.

1. Going pedantic at "A group of cells that are more similar to cells in a different brain" -- The beggining of the sentence seems to refer to ALL brains, and then it says that this group is more similar to cells in a DIFFERENT brain. These similar cells in the DIFFERENT brain are NOT part of the group? Are cell types restricted to single brains?

Some rephrashings that try and prevent that paradox, and could be what the authors mean, would be:

"A group of cells that are are more similar to each other, even among different individuals, than they are to other cells in the same individual."

Or even:

"For any individual, each cell type is defined as a group of cells that are more similar to cells in a different brain than to any other cell in the same brain."

1. The definition needs a quantifiable similarity metric. In the case of the study, it seems to be based on patterns of connectivity and morphology.

It does, however, brings up the question: what about other kinds of similarity? Are they less useful? <br /> In terms of genome content (e.g. SNPs), they will be always more similar to cells in the same individual. <br /> Does the existence of different cell types depend on the similarity metric used? <br /> Is this a contextualized, procedural definition?

These should be clear, otherwise the definition is overstating and might bring up confusion.

The algorithmic implementation you present is very data-dependent, as it considers it mandatory presence of the type in each of the 3 individual hemispheres

1. The looseness of the "similarity" ends up including cell states and other extraneous groups of cells.<br /> E.g. "apoptotic cells" are more similar to each other than to others, or "cycling cells" or even very artificial cell types such as "big neurons", whose size will be more similar to each other than cells in other brains.

You implement a particular, and complex, system based on connectivity and morphology, but these details are not part of your definition.

1. You don't make it clear if the the definition is restricted to a species or is a larger definition of cell type across nature. <br /> The different brains you mention are only in Drosophila? Or a particular species of Drosophila? A particular strain? <br /> If just a strain contains a set of cells with a particular connectivipy pattern, but other strains don't, do you _really_ want to call it a cell type?

To sum up, these notes intend on bringing up the subtleties in defining the concept of "cell type".

To make it less ambiguous one possible phrasing would be:

"We propose that cell types should be robust to inter-individual variation. Cells of the same type should be more similar to each other then to other cells. For our Fly Brain Cell Atlas, each cell type corresponds to a set of Drosophila brain neurons that are, by connectivity, more similar to neurons in other individuals then to other cells in the same brain."

If you want to talk about the concept of cell type, using some formal ontology vocabulary, you could say that:

"We define the concept of "cell type" as a group of cells for which any two cells share a similar connectivity and morphology, to a higher extent then to any other cell outside the group."

In this way, you include cells of different individuals under the same cell type umbrella, without ambiguity.

Of course, then you need to define what you mean by a "similar connectivity pattern".

It is very weird that you say that "A cell type is a falsifiable hypothesis about biological variability within and<br /> across animals34", AND that you tested such hypothesis, because that is not what you do in your system.

You have data-driven neuron types, with identifiable individuals across 3 datasets.

Now they may form each a falsifiable hypothesis. Which hypothesis is that? How can one falsify it? That is unclear, and unless you specify it, unadressable.

You mention that "Crucially, this resource includes 4,179 cell types of which 3,166<br /> consensus cell types are robustly defined by comparison with a second dataset, the<br /> “hemibrain” connectome3"

So, if your analysis FALSIFIED the hypothesis, you should not even mention the ~1000 cell types that were FALSIFIED.

You are not using the hypothetico-deductive method, and the "falsifiable hypothesis" system is not fit for your work, as it is data-driven, iterative and induction-based (and not deduction-based), without random sampling and a low N.

However, it is an excelent work! You just cannot say your resource is different becaus it treats cell types as falsifiable hypothesis to be tested, and then not rejecting in the light of falsification.

The work is, of course, solid science and adds much to the state of the art.

The theoretical formulations are still not at the same quality of the analysis pipelines and experiments.<br /> I hope at least some of these comments will be useful, with the goal of refining definitions of cell type for increased precision and usefulness.

All the best,

Tiago Lubiana<br /> tiago.lubiana.alves@usp.br

On 2017-10-06 12:51:29, user chiel wrote:

Great work, is there a Matlab version of OnAcid as well?

On 2023-12-14 19:13:06, user Sal wrote:

This insightful study unveils a unique transcriptional signature in neurodegenerative disease-associated endothelial cells, demonstrating a specific loss of nuclear TDP-43 and its impact on transcriptional alterations. These findings provide a promising avenue for understanding and potentially addressing BBB defects in AD, ALS, and FTD.

On 2017-12-13 16:36:30, user Md Nurul Islam wrote:

I think there is a different in the approaches spatially tuned cells needs to be dealt with. In our lab we prefer detecting with empty eyes and then going into computation to do further verification and characterization. This way we reduce False positive rates that may arise from doing shuffling on spatial units. Again, the whole point of shuffling the spike times is that we want to see if the results that we see (place cell, grid cell, hd cell) are random and the generation of spiking activity is particular variable dependent. Now, when it comes to grid cell, given that it has multiple firing fields, there is always a big chance that animal will be closer to or at one of the firing fields and the shuffled spike will be associated to that location mimicking the grid-like pattern and giving a 95% gridness score closer to the original spiking activity, so there may have a chance of False negative as well (I have not tested it). But when it comes to False positive, I do not see a reason not to look into the geometric locations of the autocorrelation peaks and only looking into gridness as a 'verification' tool for the grid cells. And the entire idea of 'detecting' or 'determining' or 'verifying' grid cells based on shuffling analysis, and considering it as an standard, does not need to have a proof of high false positive rate when it fundamentally may not be acceptable to do so.

On 2017-11-27 08:27:55, user Maren Cordi wrote:

Thank you for this hint and your correction. We apologize for this error<br /> and correct the citation in the revision process!

On 2020-03-04 13:54:44, user Bill Kenkel wrote:

Very cool work and very important. My only note would be to be a bit more specific in the language of the abstract. I personally had a hard time figuring out exactly what was meant by body composition being positively or negatively correlated with something. "Anthropogenic measures" being positively correlated with something still leaves the reader curious which direction the effects were and "composition" isn't a scalar quantity in my understanding. That is, if I go on a diet or eat a Thanksgiving feast, my body is not less or more composed, it is rather differently composed. It might be more clear if specific measures or takehome conclusions were included, "waist-to-hip ratio was positively correlated..." or "measures related to adiposity were positively correlated..."

I'm in the field of neuroscience but not in this subfield, so take these comments with that consideration. I hope they help and please keep up the good work.

On 2021-02-09 00:55:16, user Koji Koizumi wrote:

For the readers of the first version of this article posted March 09, 2020.<br /> I am Koji Koizumi, the first author of this article.<br /> We reviewed the results of the experiments between the first post and the current one and found two major errors. All errors have been corrected on the current version which posted September 07, 2020 and published version (doi: 10.3389/fnhum.2020.541052).<br /> Therefore, I strongly recommend reading the current version or published one.

The two major errors that I noticed and their corrections are follows:

1. Errors in calculating the rater's creativity scores.<br /> Evaluators' scores were managed on Excel. We made a mistake in specifying the range of cells for one rater's score, and the correct score was not calculated. This changed the mean of the creativity scores for the three evaluators and the correlation results with the connectivity values. For the current version and the published version, we have checked the score tallying carefully to make sure that there are no mistakes, and we have conducted a correlation analysis again to show the correct results. Specifically, two connectivity changes were positively correlated with flexibility scores. Considering the type II error, we used the FDR method for multiple comparison correction and set the q-value to the level recommended and used in previous studies.

2. Wrong way to input data for the statistical analysis of effective connectivity (iCoh) change in Pre vs. Post.<br /> To derive an iCoh value with LORETA Key Software, it is necessary to input time-consecutive EEG data. Therefore, 5 iCoh values (corresponding to the number of everyday objects comprising one AUT) were calculated for each subject and each condition (Pre/Post-Real, Pre/Post-Sham). In our previous manuscript, we used a paired t-test for the statistical comparison of Pre Vs Post, inputting the 70 number of data were entered for Pre and Post respectively (subjects (14) × iCoh values (5) = 70 data (69 degrees of freedom)). However, this was a mixture of within-subjects and between-subjects factors, and the number of data was so large that it tended to show significant differences, leading to erroneous results. To correct this error, the five iCoh values were added and averaged to derive one iCoh value for each condition, resulting in the same number of data in each condition as the number of subjects, 14. In the current version and the published version, we presented new results and observations.

Thank you,

On 2017-12-28 14:50:45, user Matteo Carandini wrote:

This seems like a really cool paper, thank you for posting it. I am going through it and Fig 2a seems to be particularly interesting, but I'd like to make sure I understand it. In the top row, what's the difference between green and black curves? I imagine they distinguish match vs. nonmatch trials, but I'd like to be sure (couldn't find this info in the caption). And in the next two rows, the firing of DA neurons, is it for the match case (where there is a reward) or all cases? What would it look like if you split it by case? If there is no reward, do you get a second peak in firing? (I guess I will figure out all this by reading on, but this info should arguably be given to the reader, no?). <br /> Best<br /> -Matteo

On 2020-03-31 15:20:48, user Søren Grubb wrote:

Dear David A. Hartmann and the Shih lab,

I want to congratulate you on a beautiful work. I know how thorough and careful you are, and this subject of whether pericytes are contractile or not, is very important – and controversial. So, I read your paper with great interest, and I am very surprised to see that<br /> you observe capillary lumen decrease when optogenetically stimulating true pericytes. I have a lot of questions and comments, so I hope you don’t mind!

I agree with your finding (or confirmation) that smooth muscle actin expression drops<br /> after (up to) 4th order capillary, as we have also found in our recent Nature communications paper: https://www.nature.com/arti.... You cite the Alarcon-Martinez et al paper to claim that there may be SMA that is not detected. I would like to make you aware that in that paper, they have a different numbering for the blood vessels, which means our 1st order capillary would correspond to their 3rd order blood vessel. Which<br /> means that when they improve their SMA stainings from 4th to 6th order vessels using their fixation procedures, it would correspond to from 2nd to 4th order capillary with our numbering. So, my conclusion is that retina might be more difficult to fixate than brain, and we do not necessarily miss out on any SMA. But I may be wrong, I often am.

I see that you have mentioned a “sphincter” in supplementary movie 5. I am not sure what<br /> your purpose of mentioning that sphincter is, I just want to let you know that that is not a typical precapillary sphincter. A typical precapillary sphincter and bulb are visible just next to the penetrating arteriole around 21 seconds into your Supplementary movie 7 where the tissue moves a little bit in the z-direction. For more info on precapillary sphincters see our paper: https://www.nature.com/arti...

For our paper we adopted your nomenclature for ensheathing pericytes on the first order capillary (which you call precapillary) because I feel that is a good description. However, your drawing of ensheathing pericytes indicated to me that they were a continuous sheet<br /> surrounding the capillary, which I found confusing until I did the confocal microscopy<br /> myself and saw that it was just because the pericyte processes are so tightly positioned<br /> around the capillary that they look like a continuous structure. I have tried to draw detailed ensheathing pericytes on Figure 1e in our NatComms paper (see link above). Maybe we all should do an effort to make more precise drawings of the mural cell morphology, like Zimmermann did beautifully in his paper: https://link.springer.com/a....

I would like to also make you aware that the smooth muscle “hybrids” as we have called<br /> the smooth muscle cells on far the most of the penetrating arterioles (with average<br /> lumen diameter around 12µm), often have a slightly bulbous nucleus, and 2-3 processes<br /> in each direction around the arteriole. So, they look significantly different from the “true” smooth muscle cells that exist on larger arterioles and arteries, which have spindle shape and an elongated nucleus.

I have never understood the reason that only the true pericyte soma should be contractile, so I’m glad to see that you address that on page 3 line 25-29. My concern has been, that because the pericyte soma protrudes it can somehow push close the capillary lumen at that exact spot if the tissue around it swells or if the capillary is somehow pulled in direction of the arteriole (by strong arteriole contraction) – and thereby false positively be interpreted as contraction.

You write that the localized two-photon optical manipulations “disentangles their local<br /> influence on capillary diameter from the influence of flow in upstream vessels”, but pericytes have gap junctions that connect them to endothelial cells, so how can you be sure if you only stimulate and observe locally? Have you tried to uncouple them with gap junction blockers?

If one capillary branch has increased RBC flux by optogenetic stimulation, does that<br /> mean the other branch has decreased flux (indicating a local effect) or does the other branch also have increased flux (indicating an upstream effect)?

Have you ever seen any indication that the blebbing you see also happens on the luminal<br /> side of the pericyte, which could push the endothelial cell towards the lumen? Are<br /> these “contractions” and blebbing by depolarization pericyte specific or would<br /> you find similar blebbing in other cells you depolarize optogenetically, for example astrocytes? Do you think the blebbing is caused by an increase in intracellular<br /> Ca2+?

You write that the blebbing might be caused by “excessive mechanical tension” when<br /> pericytes are stimulated to “supraphysiologic levels”. If the stimulation you use is not physiologically relevant but necessary for effect (in contrast to the Hill paper, that saw no effect), under what conditions do you think true pericytes contract?

If the actin cytoskeleton should be able to create a force, I guess most of the actin<br /> cytoskeleton should be organized to pull in the same direction, have you seen any indication of that?

The idea to ablate bridging pericytes is very elegant. When you ablate a pericyte, I assume<br /> it will go into apoptosis and the first thing it does is retrieve its processes and round up. Could the increased capillary lumen diameter be explained by the extra volume around the endothelial cell that a retraction of the pericyte processes leaves behind?

Thanks for a really interesting paper, it was a pleasure reading it. I really hope you find<br /> time to answer my questions.

Best regards

Søren Grubb

Lauritzen<br /> lab, Department of Neuroscience, University of Copenhagen

On 2019-08-21 00:40:37, user Govinda Poudel wrote:

This is an interesting work. Perhaps, microsleeps is causing some of the transient signal changes. Frequent microsleeps also change the dynamics of BOLD oscilliations as shown in this recent NeuroImage paper. https://www.sciencedirect.c...

On 2018-04-25 19:49:38, user Bryan Souza wrote:

We have recently read and discussed this preprint (“Dynamic Control of <br /> Hippocampal Spatial Coding Resolution by Local Visual Cues”) on our <br /> Journal Club. The paper is very well written and the results seem to be very consistent. As this is not the final version of the paper, we have written a list of comments and suggestions that we believe could improve the manuscript. I hope this can be useful for the authors.

• In some experiments, the number of animals seems to be low. Maybe it is worth doing <br /> more experiments to have at least 3 animals in each analysis. Also, does the results hold when analyzing each animal separately (e.g., in Figure 5, where the same animal performs with and without objects);

• The formula proposed by Skaggs et al., 1996 is sensitive to the basal <br /> firing rate of the cell, so that neurons with low basal firing rate are <br /> more likely to have a high spatial information (rate) measure [1]. Thus, it <br /> seems that the decrease in the spatial information found in some groups <br /> can be explained by the decrease in the basal firing rate. Is that what <br /> the authors want to show? Note that this does not mean that the cell has <br /> less spatial information (in bits) in the sense of a decoding... To say <br /> that, one could either correct the bias of the Skaggs’ metric or to see <br /> the performance of a single cell decoding..

• Could the low similarity index in the No Object condition indicate the cells are <br /> coding for something other than space? (i.e. time cells [2]).

• In the Object condition it is hard to disentangle the position of the animal from the presence of the object (even after looking for the LV cells). The new ‘place cells’ could still be coding for the object-position (as in [3]). It would be interest to also discuss this.

• Decoding accuracy values in Figure 3 are very low in comparison to other place cell-based decoders. Is this because of the spatial binning? Maybe this should be addressed in discussion.

• Is the speed of the animal in the OZ similar across the Object and No Object conditions? A different speed could explain the differences in phase precession (e.g., due to <br /> better theta phase estimation). Additionally, most of the place cells in the Object condition are in the OZs. Does the results in Figure 6 hold if the analyses are restricted to place fields there (in the OZs)?

• There is a typo in Figure 6F. The unit of theta amp should not be ‘Hz’.

References:<br /> [1] Souza, B. C., Pavão, R., Belchior, H., Tort, A. (2018) On Information Metrics for Spatial Coding. Neuroscience, 375, 62-73.

[2] Pastalkova, E., Itskov, V., Amarasingham, A., & Buzsáki, G. (2008). <br /> Internally generated cell assembly sequences in the rat hippocampus. Science, 321(5894), 1322-1327.

[3] Komorowski, R. W., Manns, J. R., & Eichenbaum, H. (2009). Robust <br /> conjunctive item-place coding by hippocampal neurons parallels learning <br /> what happens where. The Journal of Neuroscience, 29(31), 9918–9929.

Cheers!

On 2023-03-17 17:20:09, user Lasse Knudsen wrote:

This preprint has underwent major revisions and has now been published with the title "Improved sensitivity and microvascular weighting of 3T laminar fMRI with GE-BOLD using NORDIC and phase regression"<br /> https://doi.org/10.1016/j.n...

On 2025-02-18 16:22:01, user Anonymous wrote:

Dear authors,<br /> as a part of a group activity in our lab we discussed your very interesting manuscript with the goal of reviewing it as well as improving our reviewing skills. The below review is the result of this exercise and reflects thoughts and comments of several people. We hope this helps you with your way forward to publish the paper in a good journal.

Summary<br /> The manuscript by Cresto et al. addresses an important question concerning the contribution of astrocytic defects in oligophrenin-1 (Ophn1) deficiency in intellectual disability. Ophn1 is highly present at synapses and regulates the RhoA/ROCK/MLC2 pathway through its RhoGAP domain, having an important role in cytoskeleton remodelling. Previous work from the authors of this manuscript reported that constitutive Ophn1 knockout mice show deficits in synaptic transmission and plasticity, due to pre-synaptic dysfunction. Moreover, Ophn1 deficient astrocytes from those mice also display altered morphology resulting from hyperactivation of the RhoA/ROCK/MLC2 pathway. <br /> This study examines the impact of astroglial Ophn1 deficiency on synaptic transmission, plasticity, and spatial memory using a conditional, localized, and AAV-inducible approach. The researchers selectively disrupt Ophn1 in adult hippocampal astrocytes and assess astrocyte morphology, synaptic coverage, and explore two key molecular mechanisms: adenosine A1 receptor signaling and the RhoA/ROCK signaling pathway.<br /> A strength of this study is the comparison between the conditional knockdown and the constitutive KO model, which helps to confirm that some of the observed effects are specifically due to the presence of the protein in astrocytes. However, a potential weakness is that in some cases, the targeting may not have been sufficient to fully isolate the astroglial pathway, leaving room for contributions from other cell types or compensatory mechanisms.<br /> Another strength of this study is the comprehensive approach taken to investigate the effects of oligophrenin deficiency in astrocytes, encompassing behavioral experiments, electrophysiology, cellular morphology, and molecular pathways. However, the molecular pathway analysis remains incomplete, leaving some mechanistic aspects unresolved.<br /> One limitation of this study is the exclusive use of adult mice and mature astrocytes to investigate a neurodevelopmental disorder, which may not fully capture the relevant developmental mechanisms. Additionally, all experiments were conducted in murine astrocytes, with no validation in human cell lines, raising questions about the translatability of the findings.

Major Comments<br /> 1. KDastro and KDneuro verification would benefit from additional protein-level quantifications in a Western Blot or immunostaining, e.g. by using directly an anti-oligophrenin antibody or an anti-FLAG-tag antibody.<br /> 2. The measurements of alternations in the Y maze test could be described in more detail. This specific test relies on the difference between entries into consecutive different arms (i.e. ABC) and into the same arm (i.e. ABB). In the methods section, it is not completely clear if the authors discriminate between these two parameters and how they defined the "ABB" alternation. Authors could also introduce the different percentages (consecutive vs. same) into the graph to give a clearer picture of the phenotype.<br /> 3. In addition, the statement that spatial working memory is abolished results a little extreme from only these experiments. It could be defined as impaired or reduced.<br /> 4. Authors claim a reduction in presynaptic release probability, yet the first peak in Fig. 1d is similar for both WT and KDastro. The authors should clarify in the text what they mean by this statement, and how they interpret it from the data. If they mean to imply that there are more presynaptic vesicles, which are released at a lower probability, it would be good to quantify presynaptic vesicle numbers, for instance using EM.<br /> 5. Authors conclude that KDastro neurons show an increase in the activation of adenosine A1 receptors yet they don't validate this phenotype. In Fig.2, it is not clear if the effect of 8-CPT is rescuing the phenotype in KDastro neurons or simply acting on receptor activation as for WT neurons. In the full Ophn1 KO model, neurons are lacking Ophn1, unlike KDastro neurons which still express the protein. Can the authors investigate deeper on the activation of the A1 receptors in KDastro? They could assess cAMP levels via cAMP sensors (i.e. FRET-based cAMP sensors) and PKA activation by immunoblotting for its phosphorylated substrates. In addition, they could also measure adenosine release from Ophn1-KD astrocytes. This could help to define the molecular mechanism supporting the electrophysiological observations.<br /> 6. ROCK inhibitor treatment in the slices is only 20 min. Is this timeframe sufficient to induce morphological changes in the astrocytes? It would be more convincing if a corresponding actin staining was provided.<br /> 7. When ROCK is inhibited, it affects both neurons and astrocytes. Can authors discriminate that the observed neuronal effects are specifically due to ROCK inhibition in astrocytes, rather than direct effects on neurons? This is particularly relevant since ROCK inhibition is expected to mimic the presence of Ophn1, potentially rescuing the astrocytic Ophn1 deficiency.<br /> 8. For both inhibitors (8CPT and Y27632) they don’t validate that in fact inhibition works effectively and that they only target their protein of interest. They could validate this by immunoblotting downstream targets of the adenosine A1 receptor and ROCK, but also potential other off-targets that could be inhibited, like PKC in the case of Y27632.<br /> 9. “Increased branching complexity” only occurs at one specific distance of 25 microns (Fig 3.d). Generalization of this one measurement seems an overstatement.<br /> 10. Assessment of true morphology of tripartite synapses in Ophn1 KDastro can be further investigated with electron microscopy. This can help to better evaluate changes in plasma membrane and cell boundary morphology on synapses.<br /> 11. Paper would greatly benefit from an illustration of a suggested molecular mechanism.

Minor Comments<br /> 1. What is “CD8” for on figure 1b.? <br /> 2. It is not clear what “py” stands for in Fig. 1 d and f.<br /> 3. In figure 1e and 1g it is unclear how the quantification was done.<br /> 4. Figure 3, 4 and 5a-d lack information on the # of mice that were assessed, only the # of astrocytes is reported.<br /> 5. Red and dark red colouring in Fig. 5 are very hard to discern. Authors should consider other colour schemes. <br /> 6. Manuscript would benefit from describing 8CPT and Y27632 functions in the results section.<br /> 7. WT control is missing in Fig. 5e.

On 2019-03-19 12:54:58, user jeffrey stirman wrote:

Excellent work and excited to see this!. I particularly like the demonstration and discussion of potential doubling artifacts (Supp. Fig. 6) using double sided illumination (either simultaneously acquired or merged). Many home built and commercial systems don't seem to recognize this point and at times you can see it in the data (and even in published work). I have argued against simultaneous dual sided illumination since I started working with light sheet systems. Again, very nice work and a great addition for the community.

On 2023-02-11 02:32:37, user Akshaya Jayakarunakaran wrote:

Hi!

I thought the paper was a good start and raised a lot of interesting questions. However, we did have some concerns. Some of which have already been mentioned by my peers. I was particularly concerned by Fig 5. My concerns were that I did not understand why down-regulation in aged Treg results in causation for myelination regeneration. For, correlation does not mean causation.

On 2018-04-18 08:56:23, user Guillaume Rousselet wrote:

Interesting result, but a more accurate title would be:

"Spontaneous Pre-encoding Activation of Neural Patterns Pearson correlates (r=0.56, n=23) with Memory"

You could also provide a bootstrap confidence interval for the correlation. See R and Matlab code here for instance:<br /> https://www.frontiersin.org...

The bar graphs would be better replaced with scatterplots - see guidelines in these papers for instance:

http://journals.plos.org/pl...

https://onlinelibrary.wiley...

I also strongly encourage you to read this paper, to go beyond the p<0.05 cutoff to make decisions and report results:

https://arxiv.org/abs/1709....

On 2021-09-27 17:35:42, user Janis Intoy wrote:

Thanks for reading our paper! This paper was published by PNAS on September 8, 2021 and is currently available online!

On 2018-06-28 17:05:29, user Simon Schultz wrote:

This paper is in press, Neural Computation.

On 2018-05-22 18:45:07, user pb wrote:

Revised version includes a detailed demonstration of the df/f calculation, more details about the hardware ability to sustain "burst" high count rate and correction for some typos.

On 2020-09-08 18:40:44, user Divya Sitaraman wrote:

Thanks for the comment. The recurrent circuits between PAM and MBONs likely play a role in persistence of wakefulness and arousal and something that our lab is actively investigating. We will expand the discussion to incorporate these suggestions.

On 2017-01-11 20:19:41, user Stephen Van Hooser wrote:

Question: how certain can you be that the signal reflects individual boutons and is not occasionally contaminated by nearby boutons. If this situation occurred, it could produce the data that is observed as an artifact, without an actual "conversion" of an LGN cell.

On 2018-09-26 21:56:31, user Chuan-Peng HU wrote:

Not sure if it is OK to Post the comments from our decision letter:

==========================================================<br /> Dear Dr. Hu,

Thank you for submitting your manuscript to [Journal Name]. I regret to inform you that we will not be able to pursue the publication of your manuscript. While the area of research is indeed interesting, unfortunately, there were a number of serious issues raised by the reviewer, including how beauty can indeed be operationalised. Based on these concerns we will not be able to proceed with your manuscript. I am sorry that the outcome is not more positive.

Please refer to the comments listed at the end of this letter for why the decision has been reached.

We appreciate your submitting your manuscript to this journal and for giving us the opportunity to consider your work.

Kind regards,

[Editor Name]<br /> Associate Editor<br /> [Journal Name]

Comments from the editors and reviewers:<br /> -Reviewer 1

-

It will be meaningful to employ an ALE meta-analysis to discuss the neural mechanisms of visual beauty. However, some questions need to be addressed.

1. How to define visual art? The authors seem to define visual art in terms of paintings, sculptures, and visual textures. However, some other kinds of visual art, such as geometrical shape (see Jacobsen et al., 2006, Brain correlates of aesthetic judgment of beauty), dance art (see Calvo-Merino et al., 2008, Towards a sensorimotor aesthetics of performing art), and architecture (see Choo, et al., 2017, Neural codes of seeing architectural styles; Vartanian et al., 2015, Architectural design and the brain: Effects of ceiling height and perceived enclosure on beauty judgments and approach-avoidance decisions) can be included in the meta-analysis.

2. In the studies included in the meta-analyses, I found some important and classical publications are not involved in, such as, Kampe et al., Reward value of attractiveness and gaze. Nature; Kranz et al., Face perception is modulated by sexual preference. Current Biology; Ito et al., Changing the mind? Not reallyactivity and connectivity in the caudate correlates with changes of choice. Social Cognitive and Affective Neuroscience; Martin-Loeches et al., Beauty and ugliness in the bodies and faces of others: An fMRI study of person esthetic judgment. Neuroscience, and so on.

3. The “literature search and study selection”. The authors used “paintings” or “visual art” as keywords to search aesthetic studies of visual art. However, some of the publications about visual art or paintings can also be titled as “artworks” or “art”, e.g., “Viewing artworks: Contributions of cognitive control and perceptual facilitation to aesthetic experience” and “Specificity of esthetic experience for artworks: An fMRI study”. Because of this, I am not quite sure about the certainty of the results.

4. The present study tried to investigate the common beauty in the brain. However, as facial beauty is represented as natural beauty, and visual arts are represented as artificial beauty, they may be two different types of beauty. What are the common features or mental mechanisms between these two kinds of beauty should be illustrated in the Introduction.

5. It is widely accepted that OFC is a crucial node of aesthetic judgment, no matter in the visual beauty or the auditory beauty. However, this present study did not find any activation of OFC, it seems that some reasons may leaded to this some strange findings. Therefore, the authors must clearly explain possible reasons about the absence of OFC activation.

Taken together, I am inclined to reject it in the present state.

On 2020-03-31 18:58:31, user Petra Fischer wrote:

A revised and peer-reviewed version is now published in eLife: https://elifesciences.org/a...

On 2018-04-25 11:20:23, user Caleb Kemere wrote:

I can't find the figures in the PDF?

On 2016-10-26 23:16:10, user tsuyomiyakawa wrote:

We are interested in possible collaborations about measuring pH/lactate of the brain of other mouse models of these disorders. We also want to look at any mutant mice that do or do not show abnormal behaviors as a kind of their controls. If you are interested in this collaboration, please let me know. To start with, just sending us frozen brains of 5 animals for each group would be fine. We will add the data on this or another manuscript, whether brain pH/lactate is significantly changed or not. So far, at least 10 more labs have agreed with doing this collaboration. Thank you!

On 2022-07-27 19:54:13, user Martin Schrimpf wrote:

now published as a Spotlight at ICLR'22 https://openreview.net/forum?id=g1SzIRLQXMM

On 2019-07-15 00:11:45, user Daniel wrote:

This paper was published, with substantial revisions beyond the biorXiv version, as: <br /> Antoine MW, Langberg T, Schnepel P, Feldman DE. Increased Excitation-Inhibition Ratio Stabilizes Synapse and Circuit Excitability in Four Autism Mouse Models. Neuron. 2019 Feb 20;101(4):648-661.e4. doi: 10.1016/j.neuron.2018.12.026.

For those without access to Neuron content (i.e. University of California!!) please contact Dan Feldman or Michelle Antoine for full-text.

On 2018-01-05 20:32:30, user Kathy Keatley Garvey wrote:

Excellent work by UC Davis chemical ecologist Walter Leal and his lab!

On 2025-05-22 22:34:19, user Gabriel Riegner wrote:

Thank you for the comment and for sharing your papers. I revised the preprint to include them.

I also want to emphasize that our work does not claim novelty in the use of autoregressive (AR) models to estimate timescales. As stated in the preprint, AR models are used in a number of other published studies: Kaneoke et al. (2012), Meisel et al. (2017), Huang et al. (2018), Lurie et al. (2024), Shinn et al. (2023), and Shafiei et al. (2020).

Our main contribution is in the estimation of standard errors for valid statistical inference and hypothesis testing, not in the use of AR models to get point estimates. We formalize and evaluate the assumptions and theoretical properties of the method, accounting for when the fitted data is not autoregressive. Whereas your approach improves model fits to neural activity by incorporating intrinsic, seasonal, and exogenous parameters, we focused on simpler single-parameter models, emphasizing adjustments for potential misspecification to support valid inference.

I hope this addresses your concern.