On 2019-09-03 05:07:32, user Ken Takiyama wrote:

We revised mathematical equations and figures. Specifically, we added scaling factors for each tensor (i.e., the contribution of each tensor to explain original data) in the equations and figures.

On 2021-11-11 22:06:11, user Flavia Mancini wrote:

Very interesting. It would be good to add results of whole-brain analyses too, perhaps as supplementary materials.<br /> You might want to check out these two previous study from Ben Seymour's group:<br /> https://www.sciencedirect.c...<br /> https://elifesciences.org/a...<br /> You could also use a reinforcement learning model to capture neural activity associated with the pain PE - this would give you a trial-by-trial regressor for the pain PE, and it might be more sensitive than the traditional, axiomatic approach.

On 2016-04-07 09:53:59, user Paul Whiteley wrote:

Just a few points: would it be possible to provide (a) assay details for the determination of urinary creatinine, and (b) some data on the levels of creatinine obtained? The reasoning: we've previously suggested that urinary creatinine levels might be 'altered' in some cases of autism: http://www.ncbi.nlm.nih.gov... as have others: http://journals.plos.org/pl... There's nothing so far in the peer-reviewed literature about creatinine levels in parents of children with autism but given how altered creatinine concentrations can affect other corrected metabolites, it might be something useful to present. <br /> Interesting data by the way, many thanks. Paul Whiteley

On 2021-04-14 20:58:27, user Anna Victoria Kasdan wrote:

This review was completed as part of the SfN Reviewer Mentor Program (mentee: Anna Kasdan, Vanderbilt University; mentor: Dr. Andre Cravo, Center for Mathematics Computation and Cognition at the Federal University of ABC).

Summary

This manuscript investigated the psychophysiological mechanisms underlying talker discontinuity in an auditory digit span task. Using EEG and pupillometry, the authors found that talker discontinuity (i.e., mixed talker condition) elicited a P3a-like ERP component, decreased alpha power during a working memory retention interval, and increased pupil dilations during the encoding period for the faster stimulus presentation rate (0-ms ISI). The authors situate their results within a stimulus-driven attention and auditory streaming framework. The authors present their data, both visually and in written form, clearly and concisely and logically walk through the goals and hypotheses of the study and present clear explanations of the observed results. Overall, this manuscript is well-written, easy to follow, and an important step forward in understanding the neural basis of talker variability. All of the points included below are minor suggestions and points of clarification.

Introduction<br /> • The goals and hypotheses presented on page 4 are extremely clear and straightforward. <br /> • It might be helpful to explicitly state which of the ISI conditions (500-ms vs 0-ms) is the discontinuous temporal condition. This might not be immediately clear to the reader (one could think that the 0-ms condition is where digits are presented “on top of each other” or something and the 500-ms is where they are evenly spaced apart). It was clear, however, that the mixed talker condition was the discontinuous talker condition. Additionally, in the last sentence of the introduction there is a typo (should be 500-ms, not 500-s).

Methods<br /> • Please include the citations for Psychtoolbox (Brainard 1997; Kleiner, Brainard, Pelli, et al. 2007). (last paragraph on pg. 6). <br /> • What was the rationale for not including by-participant random slopes in addition to by-participant random intercepts in the mixed effects models for behavior and pupillometry?<br /> • It is not clear in the text whether 60 Hz line noise was removed from the EEG data. If this step was included in the pipeline, it would help the reader if authors gave more details of how this was done (filter or line noise ICA components). <br /> • It would help the reader if the authors could specify 1) the size of the fixed window length for the Hanning taper and 2) neighboring parameters used in the clustering analyses. <br /> • The authors state at the beginning of the Time-Frequency analysis section that data were re-referenced to the average. Was this also the referencing procedure for the ERP data? <br /> • Was the baseline period for the TFRs a condition average across all 4 conditions (2 talker and 2 temporal discontinuities)? This could be reworded to make it clear which specific conditions went into the baseline normalization step. <br /> • The authors’ use of the data-driven, clustering based statistical approach to answer their EEG-based questions of interest is robust and methodologically sound. One clarification point for the TFR clustering: was there a dimension (electrodes, time, frequency) that the authors averaged over?

Results<br /> • In general, the authors provide comprehensive details about the methods/results of the behavioral data (e.g., explicitly stating the dependent measure, how categorical factors were coded). <br /> • The use of SEM bars in Figure 4 (ERP data) is very helpful for the reader. <br /> • Do the authors have any thoughts about the decay of the P3 component for the mixed vs. single talker conditions? It appears from Figure 4 that there are differences in the decay rate of the P3 component between the two conditions (longer decay for the mixed-talker), in addition to differences in amplitude. <br /> • A very minor point, but it would be helpful to include the F value from Table 2 when referring to the temporal discontinuity x digit position results in the text (in addition to the beta value) so the reader can easily map between text and table results. In general, the inclusion of both tables with the model results and the corresponding text was very helpful.

Discussion/Conclusion<br /> • Reframing how the authors discuss their previous behavioral study from 2019 (second paragraph, pg. 19) might help the reader. I would suggest for authors to start this paragraph with how the present study is consistent with the 2019 study (instead of starting with how it might be at odds with it). From my understanding it seems that the 2019 study is not as “at odds” with the results from the current study as the authors initially convey. The authors do highlight the differences between the two studies, which is important (RT and the overall efficiency measure could not be measured in this study while EEG/pupillometry was recorded). A slight rewording of this paragraph might help in emphasizing that the two sets of behavioral results are not so different from one another (i.e., when using accuracy as the DV in both studies, there is a significant effect of talker, but neither significant effects of temporal discontinuity nor significant interactions). <br /> • The paragraphs discussing the N1 ERP component in the context of talker discontinuity are very clear. One might have expected differences in amplitude for early components between the two different ISI conditions. Do the authors have any predictions about why there were no observed differences between the ISI conditions for early ERP components? <br /> • Though the alpha results are slightly counterintuitive, the authors provide a nice and reasonable alternative explanation for why there was a decrease, rather than an increase, in alpha power for the mixed vs. single-talker speech conditions during the retention period.

Minor Points<br /> • Typo on top of page 8: “did not differ in across the experimental conditions”<br /> • Typo on top of page 21: “reorient listeners’ attention to the acoustic features”

On 2017-04-15 12:33:59, user AdamMarblestone wrote:

-"A Unified Dynamic Model for Learning, Replay, and Sharp-Wave/Ripples" http://www.jneurosci.org/co...<br /> -"Dendritic Spikes Provide a Mechanism for Hippocampal Replay and Sharp-Wave/Ripple Generation" http://www.jneurosci.org/co...<br /> -"The ventral basal ganglia, a selection mechanism at the crossroads of space, strategy, and reward." https://www.ncbi.nlm.nih.go...<br /> -"Iterative free-energy optimization for recurrent neural networks (INFERNO)" http://journals.plos.org/pl...

On 2017-03-17 05:51:38, user AdamMarblestone wrote:

-"Stellate cells drive maturation of the entorhinal-hippocampal circuit" http://science.sciencemag.o...

On 2016-12-29 04:02:13, user AdamMarblestone wrote:

-"Neurogenesis Deep Learning" https://arxiv.org/abs/1612....

On 2017-10-21 23:22:47, user AdamMarblestone wrote:

-"Specific hippocampal representations are linked to generalized cortical representations in memory" https://www.biorxiv.org/con...

On 2017-03-09 04:02:26, user AdamMarblestone wrote:

-"Population Coding in an Innately Relevant Olfactory Area" http://www.cell.com/neuron/...

On 2016-12-20 15:38:35, user AdamMarblestone wrote:

-"Learning Features by Watching Objects Move" https://arxiv.org/pdf/1612....

On 2017-12-20 22:12:50, user AdamMarblestone wrote:

-"Equivalence of Equilibrium Propagation and Recurrent Backpropagation" https://arxiv.org/pdf/1711....

On 2016-11-30 19:12:29, user AdamMarblestone wrote:

-"State-Dependent Spike-Timing Relationships between Hippocampal and Prefrontal Circuits during Sleep" http://www.sciencedirect.co...<br /> -"Decoupling through Synchrony in Neuronal Circuits with Propagation Delays" http://www.sciencedirect.co...

On 2017-06-21 22:06:49, user AdamMarblestone wrote:

-"Programmable Agents" https://arxiv.org/abs/1706....<br /> -"Grounded Language Learning in a Simulated 3D World" https://arxiv.org/abs/1706....

On 2017-09-13 19:48:32, user AdamMarblestone wrote:

-"Switching between internal and external modes: a multi-scale learning principle" http://www.mitpressjournals...<br /> -"Dynamic Routing Between Capsules" https://nips.cc/Conferences...

On 2017-06-13 02:33:43, user AdamMarblestone wrote:

-"CortexNet: a Generic Network Family for Robust Visual Temporal Representations" https://arxiv.org/abs/1706....

On 2017-06-14 01:22:31, user AdamMarblestone wrote:

-"Temporally Efficient Deep Learning with Spikes" https://arxiv.org/abs/1706....

On 2022-11-02 17:04:25, user Chengwen Zhou wrote:

This preprint has been published by Brain Communications with figure changes and new addition data and a link will be forthcoming. By Chengwen Zhou

On 2025-04-26 15:32:51, user hsw28 wrote:

Congratulations on this exciting work — it's great to see further exploration of how manifold structure can flexibly encode aversive information while maintaining spatial representations.

I wanted to point out that very similar findings were reported in our recent preprint ( https://pmc.ncbi.nlm.nih.gov/articles/PMC11527332/ , posted October 2024), where we showed that alterations to a shared manifold encode the presence of an aversive stimulus without disrupting spatial structure.

As your preprint develops toward formal publication, I would appreciate if you could cite our work to acknowledge the prior report of these findings. Here's the link for easy reference: PMC11527332.

Looking forward to seeing where this line of research goes!

On 2020-05-20 09:07:37, user Wirth M wrote:

Very nice study and great results! Convergent also what we found https://www.biorxiv.org/con....

On 2018-12-10 16:49:34, user ezra smith wrote:

Are you able to analyze any control electrodes from brain regions you would expect to be unaffected by auditory tones (varying in frequency, location, etc)?

On 2024-09-18 18:22:33, user Xihuimin Dai wrote:

This paper is now published in hLife doi: 10.1016/j.hlife.2023.10.004<br /> and has another preprint version at https://www.biorxiv.org/content/10.1101/2022.11.17.516975v1

On 2021-10-13 02:42:25, user ????(Shinji Masuda) wrote:

Good work. However, the authors need to check our recent paper published last year that already showed that ppGpp is present in metazoa. <br /> https://www.nature.com/arti...

On 2018-10-18 14:40:57, user Joseph Griffis wrote:

Dear Ito et al.,

I am happy to see this paper -- this is an extensive and systematic comparison of different publicly available lesion segmentation approaches that nicely highlights the strengths and weaknesses of the different approaches, and that also provides clear directions for how future tools might improve their performance.

I have one comment and a couple of questions, though:

My comment is that in the Abstract, it is stated: "...whereas the Gaussian Bayes method had the highest recall/least false positives (median=0.80)". I think that you mean "highest recall/least false negatives", as the method with the highest precision should also have the lowest false positive rate (i.e. LINDA). Indeed, the Discussion (section 4.1) it is stated that both lesion_gnb and ALI had higher false positive rates than LINDA, and so I believe that this is simply a typographic error .

My questions regard the use of the re-segmentation step for lesion_gnb, as I was surprised to see that you found it actually worsened performance in your dataset. In the Supplement, it is stated that the re-segmentation step was used, but no details are given. Specifically, I was wondering: (1) which lesion map was used as the additional tissue class (i.e. was it the re-smoothed and continuously valued post-processed map or the binarized post-processed map), (2) what tissue probability threshold was used for binarizing the output of the re-segmentation step, and (3), whether additional post-processing was performed on the output of the re-segmentation.

In my experience, the re-segmentation provides better performance when (1) the additional tissue class is defined by re-smoothing the binarized post-processed (e.g. smoothed, cluster-thresholded) lesion map (without implicit masking; while I have the option in the toolbox because it is an input argument to SPMs smoothing function, I honestly don't think that it should ever be used in this context as one of the main purposes of smoothing here is to wash degrade small clusters that are surrounded by zero-valued voxels) so that it is continuously valued -- this has the effect of down-weighting both small false positive clusters and the edges of the lesion map in the re-segmentation, (2) a relatively high (e.g. 0.6-0.75) threshold is applied to the output of the re-segmentation (I typically do this manually as the "best" threshold varies for each case, though, so I can see why this might not have been done here), and (3) cluster-thresholding (and perhaps light smoothing) is applied again.

Again, I am happy to see this impressive and timely work, and I appreciate any time that you spend replying to these comments/questions.

Best,

Joseph

On 2019-09-27 14:20:13, user Speedy wrote:

Love the idea. But can someone explain to me why the brain is the main focus? Isn't the brain least understood part of the human body? Why not start with simple things like low-latency receptors in the spine? Imagine being able to control the mouse on screen with your brain... Or have devices that allow to speed up the reaction times. 2/3 of reaction time is actually pressing the button, what if you don't "need" to even touch it. so many uses...

On 2019-12-27 12:19:43, user Alexander Seifalian wrote:

Currently we moved to preclinical study

On 2019-09-15 19:51:10, user Zi7ar21 wrote:

Wow! What part of the paper describes how it works? I would presume everyone's brain is different and so to get it to work you would like train a neural network to do actions based on data from the brain trying to move those artificial limbs, until finally the network learns to do whatever with input data from the electrodes. Very cool!

On 2019-11-14 12:39:41, user jayanth dn wrote:

on solving schizophrenia can a AI chip from Tesla generate empathy. is it all about processing information only ?then how can math legends like john nash suffer from schizophrenia .

On 2020-06-24 18:48:44, user Wayne Frankel wrote:

A final, post-peer reviewed version of this manuscript is now available online at the journal Brain: PMID: 32577763 DOI: 10.1093/brain/awaa147

On 2023-03-23 10:03:53, user Asaph Zylbertal (@azylbertal@n wrote:

A revised version is now out on eLife:<br /> https://elifesciences.org/a...

On 2023-02-18 13:44:17, user Scott Makeig wrote:

19th-century instrumentation recorded the button press response as a single 'trip-switch' moment - an 'instantaneous event' (physicists might scowl here). Unfortunately, this RT-view has been carried down through experimental psychology, cognitive science, and now cognitive neuroscience. However, attaching EEG plus an EMG channel easily documents that the action of pressing a button requires just as much brain-brain-body interaction as any other intentional act (save within-behavior shaping in more time-extensive actions) -- and is an event that unfolds through time (like any other event), likely with as much trial-to-trial variability as any other impulsive action event. Using fMRI alone, the details of its unfoldment are of course unavailable, but the metabolic consequences may be, if one looks carefully.

On 2024-09-30 15:48:21, user Renzo Huber wrote:

The manuscript entitled “Feasibility of laminar functional quantitative susceptibility mapping” describes a 7T methods study that investigates whether fQSM can capture layer-specific signal changes and whether it has the potential to be less venous bias compared to Gradient Echo BOLD.

The authors used a SIEMENS WIP sequence of segmented 3D-EPI at a Terra scanner with 0.8mm isotropic resolution during a finger tapping task to see if they can detect previously reported activation in the upper and deeper layer of human M1.

The study combines advanced hardware (novel 7T) with advanced sequences (segmented 3D-EPI) and advanced processing (phase-based QSM and laminar signal pooling). Each step is of interest to the MRM readership. <br /> The study is overall well described. And the study design is clear. I believe that every design choice is justified and relevant for the overall research question: feasibility of laminar fQSM.

In the emerging field of layer-fMRI, there is a big research focus on finding acquisition and analysis methods with the best compromise of sensitivity, specificity, and feasibility. There are about 70 papers about this topic published already. The manuscript at hands augments this literature with a modern set of tools and innovative combination of established methods.

The layer results are a bit messy. Layer profiles in Figs. 4 and 6 look quite noisy. This is partly expected for the ambitiously accelerated protocols used here (GRAPPA 6 and 12). The results are consistent enough to claim “feasibility of fQSM”. No group average layer profiles are shown (unclear if group results would be clearer?!?).

I recommend the publication of this manuscript with a few small suggestions of revisions to be considered. I list them below:

1.) It is not really clear to me how the M1 ROI was selected. Some previous studies exclusively focused the analysis on the evolutionary older part of M1 (BA4a). (e.g. outlined here: https://layerfmri.com/2018/03/13/finding-roi-of-the-double-layers-in-m1/) "https://layerfmri.com/2018/03/13/finding-roi-of-the-double-layers-in-m1/)") . Maybe the authors can state how they defined their ROIs?

2.) It is not trivial to obtain interpretable phase data with EPI. given that the authors use CAIPI field of view shifting, they must use the icepat reconstruction from SIEMENS. This framework does not allow saving RF channels uncombined. So it might be helpful to the reader if the authors can state which coil combination method was used?<br /> Some of the default EPI Nyquist phase correction methods calibrate the global phase on a TR by TR basis. This would make it harder to interpret functional phase changes. Maybe the authors can mention in the manuscript, which phase correction was used.

3.) For some of the protocols, Partial Fourier was applied in two directions. This is surprising to me. In my understanding, partial Frontier imaging is based on the assumption that when the image is real (no imaginary part), k-space is point-symmetric. This can be helpful to obtain magnitude data in Partial Fourier without much loss in resolution. But I am not sure if this also applies to phase data. Doing partial Fourier with phase imaging sounds like reconstructing a phase with an algorithm that assumes that there is no phase in the first place. <br /> Doing additional partial Fourier in the second phase encoding direction is even more surprising to me. The k-space symmetry is a point symmetry, not an axis symmetry. This means that doing a double partial Fourier results in complete loss of high resolution information in two diagonal directions. Maybe I am misunderstanding something. If authors agree with my worries, they could include a statement in the manuscript stating that their resolution might be affected by Partial Fourier imaging. Maybe they can state in the manuscript which partial Fourier algorithm was used? I believe POCS is not available in IcePat?

4.) The acquisition protocol is relatively ambitious. With extremely large coverage (80-212 slices) and high acceleration factors (GRAPPA 6-12). In the field of layer-fMRI, there are currently only first cautious attempts in the field to get towards whole-brain imaging. But due to the high noise level of these protocols, whole brain layer fmri required up to 51 runs (e.g. see Kenshu dataset). Here, the acquisition protocol is covering large parts of the brain, while ‘only’ activating the sensory motor system. This means that the feasibility test investigated here refers to a worst case scenario for generalizable acquisition protocols. For more tailored, brain-area-specific protocols with smaller FOV and with less required acceleration, and shorter TRs, the results might be even more robust.

5) Stylistic comment: <br /> Figures 1,3,5,6 use the term “fMRI” as GE-BOLD. I think this could be confusing. Technically fQSM and VASO are also fMRI. Similarly in Tab. 1 BOLD is referred to at 3D-EPI, whoever VASO is also using 3D-EPI. The authors could consider referring to it as BOLD/fQSM?

On 2021-01-14 00:45:45, user Sudhakar Tummala wrote:

This preprint is accepted in IEEE ISBI 2021. Link <br /> will be given once it is available in IEEE Xplore.

On 2025-09-02 12:07:37, user Constant VINATIER wrote:

Feedbacks about your preprint : https://doi.org/10.1101/2025.08.11.669634

About registration: <br /> We could not find any information about the pre-registration of your study in the pre-print. Pre-registration involves documenting the hypotheses, methods, and/or analyses of a scientific study prior to its conduct (10.1073/pnas.1708274114; 10.1038/s41562-021-01269-4). If your study was pre-registered, we strongly encourage you to include the registration number in the pre-print, ideally in the abstract make this important information easy to retrieve, as this practice enhances transparency and reproducibility. If the study was not pre-registered, this should be acknowledged as a limitation. For future studies, we recommend pre-registering on an appropriate repository.<br /> About Protocol Sharing: <br /> We did not find the protocol for your study. If you have one, we encourage you to share it as supplementary material or deposit it in a publicly available repository such as the Open Science Framework ( https://osf.io ) or Zenodo ( https://zenodo.org/) "https://zenodo.org/)") . You can then include a statement in the Methods section indicating that your protocol is openly available (e.g., 'The protocol for this study is available at (link)/ in the supplementary'). Sharing your protocol will help readers better understand your study and enable them to reproduce it if they wish to test it.<br /> About the Statistical Analysis Plan Sharing: <br /> We did not find the Statistical Analysis Plan (SAP) for your study. If you have one, we encourage you to share it as supplementary material or deposit it in a repository such as the Open Science Framework ( https://osf.io ). You can then include a statement in the Methods section indicating that your protocol is openly available (e.g., 'The SAP for this study is available at (link)/ in the supplementary'). Sharing your SAP will help readers better understand your study and enable them to reproduce it if they wish to test it.<br /> About Deviations and/or changes<br /> We could not find any information about potential deviations or changes to the protocol in your pre-print. Since such deviations are common, if this applies to your study, we strongly encourage you to include a subsection titled Changes to the Initial Protocol in the Methods' section and discuss these changes as a potential limitation of your results. If any deviations occurred during your study, please specify them in this new subsection.<br /> About Data sharing / FAIR Data<br /> We found insufficient information about your data sharing approach. Data should be findable, i.e. data are to assigned a globally unique and persistent identifier (for instance there is a DOI assigned to the dataset, or data are registered or indexed in a searchable resource). Data should also be accessible, i.e. data are retrievable by their identifier and can be accessed following an open, free, and universally implementable protocol. As your data id not sensitive data, we encourage you to share it openly on a data sharing repository (Dryad, etc.) and include the Digital Object Identifier (DOI) in the Methods section. If you want more information about good practices of data sharing, visit https://www.go-fair.org/ <br /> About Code sharing<br /> We could not find any information about your (statistical) code. Sharing code is important for enhancing transparency and reproducibility, especially since it does not contain sensitive information. We encourage you to openly share it on a code sharing platform (Github, Codepen, CodShare, etc.) and include the Digital Object Identifier (DOI) in the Methods section. If you want more information about Code sharing https://fair-software.nl/ .

On 2023-04-01 18:20:53, user Eric wang wrote:

For this publication, the afflictions of Dr. Lei Wang is:

Key Laboratory of Ion Beam Bioengineering, Hefei Institutes of Physical Science, Chinese Academy of Sciences and Anhui Province, Hefei, Anhui 230031, People’s Republic of China.<br /> Current address: Department of Physiology and Biophysics, Miller School of Medicine, University of Miami, Miami, FL 33136, USA.

On 2020-07-03 13:59:30, user William Matchin wrote:

Interesting data but the title of the paper appears to conflict with the statistical results: they do report a significant region x stimulus interaction. This was particularly driven by the MFG ROI, as when they removed it, the interaction effect didn't reach significance (p = 0.16).

However, I would be *very* curious to see other analyses/data addressing more hypothesis-driven comparisons. For instance, the difference between sentence and word list in terms of the inter-subject correlation is quite strong in the posterior temporal/inferior parietal ROI and the pars orbitalis ROI, whereas this difference is much smaller in the anterior temporal ROIs. If more tighter comparisons are done, removing other ROIs and other conditions (such as stories, paragraphs, and reverse audio), such interactions would likely be significant.

On 2020-10-27 13:04:14, user Michael Shadlen wrote:

The findings connect Hal Pashler's central bottleneck and the psychological refractory period to perceptual decision making. I suspect the buffer is George Sperling's iconic working memory. More generally, the study bears on the temporality of thought, and I predict it will lead to a revision in our understanding of the neuroscience of decision-making (work in progress). If you read and have comments/suggestions, please share by email or here.

On 2021-03-10 14:28:37, user Yul HR Kang wrote:

Now online at eLife: https://elifesciences.org/a...

On 2020-11-26 00:22:04, user Noriko Osumi wrote:

This work has now been accepted in EMBO Reports. This preprint is not the final version.

On 2020-12-16 20:02:01, user Gabriel Koch Ocker wrote:

We were recently made aware of an error in this article: in Figure 13, <br /> we neglected several one-loop contributions to the two-point <br /> correlation. For the networks we studied here, these contributions are <br /> small (third order in the coupling strength). For further discussion, <br /> please see the correction note appended to the arXiv version at https://arxiv.org/abs/1610.....

On 2021-10-05 18:01:53, user Mack Yang wrote:

This manuscript is now published in Brain Stimulation with different title: 'Sonothermogenetics for noninvasive and cell-type specific deep brain neuromodulation'.<br /> https://www.brainstimjrnl.c...

On 2018-08-21 04:24:12, user James Baker wrote:

Looking summaries of large and recent studies focusing on whether tDCS is effective in treatment of depression, insomnia, and anxiety. I want to also learn whether the stimulate one part of the brain but depressing another part, problem has been solved. I want to see this type therapy succeed but I also want to see it achieve scientific credibility.

On 2024-07-31 10:58:13, user Jose Maria Delgado Garcia wrote:

This paper has been already published in Cerebral Cortex, 2024 Jul 3;34(7):bhae271<br /> https://doi.org/10.1093/cer...

On 2019-06-07 20:21:55, user peakqi wrote:

we can use a simple weight summation decoder to extract object identity from monkey iT(inferior temporal cortex) neuron ensemble activation, referring to James DiCarlo's paper of 'https://www.cell.com/neuron.... Similarly, the paper used a simple weight summation decoder to identify digit identity. If the network did not disentangle the image information, such a linear summation decoder could not decode the object identity.

On 2024-10-01 09:13:21, user Lukas wrote:

this study is now out in Nature: https://www.nature.com/articles/s41586-024-07898-9

On 2020-03-16 15:42:55, user david robbe wrote:

The last sentence of the manuscript is missing in version1 of the ms :(<br /> It should finish with :<br /> Future studies should investigate whether signaling effort and urgency are the two sides of a unique function implemented in the basal ganglia to maximize the reward rate while minimizing costs.<br /> This will be corrected .....

On 2020-12-11 07:34:36, user Noriko Osumi wrote:

This work has been accepted in Molecular Brain. This version is not a final one.

On 2024-08-29 09:28:00, user Lidia Alonso wrote:

This article has been accepted and published in Cerebral Cortex:<br /> https://academic.oup.com/cercor/article/34/8/bhae312/7727877 <br /> https://doi.org/10.1093/cercor/bhae312

On 2023-12-13 17:02:48, user YASEMIN OZAY wrote:

As someone with limited background knowledge on neuroscience, I really enjoyed reading this paper and found it easy to follow. The organization of the paper allowed me to understand some of the intricate cellular processes that take place during synapse development and the presynaptic role of LRP4. Here are some general comments I had regarding the paper:

1. I liked that LRP4 expression at the pre-synaptic NMJ was confirmed using multiple experimental techniques including the LRP4-GAL4 driver for GFP expression, the CRISPR-cas9 mediated 3-HA epitope tagging and the co-staining with Brp. I believe that these results provided a strong foundation as to why LRP4 may serve functional and developmental roles near the active zone.

2. I found it helpful that the confocal images were accompanied by quantifications of what the researchers were looking at. I find that usually when there is too many images from staining, the reader can get confused on what exactly the focus is. The quantifications in this paper made it much easier to follow the results.

3. One thing that I was confused about was the sample sizes. I realized that multiple NMJs were taken from the same larvae in most of the figures and wasn’t sure how this would reflect on the results since they can’t be treated as independent data points. I would suggest using a nested-ANOVA or averaging the pseudo replicates per larvae to statistically account for this.

4. I would also prefer to see statistical differences between the experimental groups. I realized that in most figures the experimental groups were being compared to the control. I believe it would be a good way to confirm results if experimental groups were also compared.

Overall, I think this was a great paper and provides great insights into the role of LRP4.

On 2025-10-07 19:34:11, user someone wrote:

where's the supplementary figures?

On 2020-09-03 15:39:56, user George Matsumoto wrote:

I think that you probably should have credited Raskoff et al for the Kreisel system that you used. Raskoff, Kevin & Sommer, Freya & Hamner, William & Cross, Katrina. (2003). Collection and Culture Techniques for Gelatinous Zooplankton. The Biological bulletin. 204. 68-80. 10.2307/1543497.

On 2025-06-29 01:17:23, user SD wrote:

The search for an optimal stimulation control input that ensures the transition of a given neural system from an initial state to a target state—where the modeling is performed using a generative modeling method—is of utmost importance [1]. Recently, the Koopman formalism has been used to convert the nonlinear generative model into an equivalent linear system with the aid of an infinite-dimensional operator [2], facilitating the design of optimal control inputs [3]. However, the author believes that any control input (including an optimal one) can only be designed if the nonlinear generative model is controllable. If the nonlinear generative model is controllable, then its equivalent linear system representation is also controllable. We now state some expressions of accessibility for nonlinear discrete-time systems, as proposed in [4,5,6].

\textbf{Theorem 1:} Consider a nonlinear discrete-time system of the form <br /> \begin{align}<br /> z(t+1)=f(z(t),u(t)),\,\,\forall\,\, z(t)\in\mathbb{Z}, u(t)\in\mathbb{U}<br /> \label{eq1}<br /> \end{align}<br /> The state--set $\mathbb{Z}$ is a connected manifold of dimension $n$ and $\mathbb{U}\subset \text{clos}\,\,\text{int}\,\,\mathbb{U}$ which is also connected in nature. If the system given by Eq. (\ref{eq1}) is analytic then the following properties hold true<br /> \begin{itemize}<br /> \item The system is \textit{forward} accessible if and only if<br /> \begin{equation}<br /> \text{dim}\,\,\mathcal{L}^{+}(z)=n<br /> \end{equation}<br /> \item The system is \textit{backward} accessible if and only if<br /> \begin{equation}<br /> \text{dim}\,\,\mathcal{L}^{-}(z)=n<br /> \end{equation}<br /> \item The system is \textit{transitive} if and only if<br /> \begin{equation}<br /> \text{dim}\,\,\mathcal{L}(z)=n<br /> \end{equation}<br /> \end{itemize}<br /> For further details on how to calculate the Lie derivatives, please see [4].

\textbf{Remarks:} The paper cited in [3] for designing optimal control input through the lens of Koopman operator theory is [7], which assumes that the original nonlinear system is controllable. If the assumption is true, then it is always possible to reach from any initial state to any final state under the influence of a finite control input within a finite amount of time. If some states are unreachable, due to physical constraints on the control input as claimed in [3], then the original assumption that the nonlinear system is controllable is not true, and the design procedure proposed is not valid.

\vspace{5mm}

Now, if the nonlinear system is not controllable, then we need to look in the direction of reachability. Reachability analysis aims to determine all possible future states that a system can attain, starting from uncertain initial conditions and incorporating any system uncertainties. The reachability analysis of linear systems and nonlinear continuous systems for which dynamics is known has received a great deal of attention in the literature [8]. Recently, the concept of matrix measures has been extended to systems on time scales (which includes systems on continuous, discrete or hybrid systems) [9]. Thus, the reachability analysis of discrete--time systems can be proved using its corresponding matrix measures. As an alternative approach, one can look in the direction of constrained zonotypes which has been proposed to perform reachability analysis of nonlinear discrete--time systems [10].

References:<br /> 1) Rishikesan Maran, Eli J M¨uller, and Ben D Fulcher. Analyzing the brain’s dynamic response to targeted stimulation using generative modeling. Network Neuroscience, 9(1):237–258, 2025.<br /> 2) Petar Bevanda, Stefan Sosnowski, and Sandra Hirche. Koopman operator dynamical models: Learning, analysis and control. Annual Reviews in Control, 52:197–212, 2021.<br /> 3) Anandita De, Roozbeh Kiani, and Luca Mazzucato. Towards model-based design of causal manipulations of brain circuits with high spatiotemporal precision. bioRxiv, pages 2025–05, 2025.<br /> 4) Bronislaw Jakubczyk and Eduardo D Sontag. Controllability of nonlinear discrete-time systems: A lie-algebraic approach. SIAM Journal on Control and Optimization, 28(1):1–33, 1990.<br /> 5) Dorothee Normand-Cyrot. Th´eorie et pratique des systemes non lin´eaires en temps discret. PhD thesis, 1983. 6) Salvatore Monaco and D Normand-Cyrot. D´eveloppements fonctionnels pour les systemes non lin´eaires en temps discret. Consiglio nazionale delle ricerche-Istituto di analisi dei sistemi ed . . . , 1984.<br /> 7) Milan Korda and Igor Mezi´c. Linear predictors for nonlinear dynamical<br /> systems: Koopman operator meets model predictive control. Automatica,<br /> 93:149–160, 2018.<br /> 8) John Maidens and Murat Arcak. Reachability analysis of nonlinear sys-<br /> tems using matrix measures. IEEE Transactions on Automatic Control,<br /> 60(1):265–270, 2014.<br /> 9) Giovanni Russo and Fabian Wirth. Matrix measures, stability and con-<br /> traction theory for dynamical systems on time scales. arXiv preprint<br /> arXiv:2007.08879, 2020.<br /> 10) Brenner S Rego, Guilherme V Raffo, Marco H Terra, and Joseph K Scott. Reachability analysis of nonlinear discrete-time systems using polyhedral relaxations and constrained zonotopes. In 2024 IEEE 63rd Conference on Decision and Control (CDC), pages 7032–7037. IEEE, 2024.

On 2025-06-11 00:53:03, user Desmond Smith wrote:

This preprint employs physical "voxelization" to map mitochondrial activity in the human brain and makes parallels with imaging technologies such as PET and MRI.

However, the study does not reference a series of >10 papers published by us commencing some 23 years earlier, in which voxelation was used to create transcriptomic and proteomic maps of the mouse and human brain. Similar to the preprint under consideration, our papers are replete with analogies to biomedical imaging methods.

Our papers include:

Brown et al., High-throughput imaging of brain gene expression. Genome Res. 2002 Feb;12(2):244-54. ( https://doi.org/10.1101/gr.204102) "https://doi.org/10.1101/gr.204102)") .

Brown et al., Multiplex three-dimensional brain gene expression mapping in a mouse model of Parkinson's disease. Genome Res. 2002 Jun;12(6):868-84. ( https://doi.org/10.1101/gr.229002) "https://doi.org/10.1101/gr.229002)") .

Singh et al., High-resolution voxelation mapping of human and rodent brain gene expression. J Neurosci Methods. 2003 May 30;125(1-2):93-101. ( https://doi.org/10.1016/s0165-0270(03)00045-1) "https://doi.org/10.1016/s0165-0270(03)00045-1)") .

Petyuk et al., Spatial mapping of protein abundances in the mouse brain by voxelation integrated with high-throughput liquid chromatography-mass spectrometry. Genome Res. 2007 Mar;17(3):328-36. ( https://doi.org/10.1101/gr.5799207) "https://doi.org/10.1101/gr.5799207)") .

On 2017-11-07 11:38:40, user CT Hsu wrote:

Loved the paper, thanks! I was wondering if the y-axis in panel 1F should be labeled "Night sleep" rather than "Day sleep"; otherwise, I'm not sure I follow why 1E and 1F are different.

On 2025-05-06 14:16:13, user Anusha Kamesh wrote:

This paper has been revised and published in npj Parkinson's. Check out the most up to date version here: https://www.nature.com/articles/s41531-025-00948-7

On 2018-02-13 16:38:42, user Tom Faust wrote:

Great paper thank you for posting it. Quick question: did you deliver any omissions with the silenced solenoid? Figure 3 is very interesting, but I'm not observing a similar predictive response (increase in firing rate) prior to the decrease in firing following omission in figure 5. Thanks!

On 2021-06-03 01:02:09, user marci_rosenberg wrote:

Precise and pervasive phasic bursting in locus coeruleus during maternal behavior<br /> Roman Dvorkin, Stephen D. Shea<br /> Biorxiv, April 1st, 2021<br /> Doi: https://doi.org/10.1101/202...<br /> Reviewed by Eszter Kish and Marci Rosenberg as part of the 2021 UCSF Peer Review minicourse with James Fraser

Summary<br /> In the central nervous system, noradrenergic signaling has been implicated in a wide variety of functions, including arousal, learning and memory, and, as this paper highlights, maternal behavior. While acute bursting of noradrenergic neurons has been shown to play an important role in goal directed behaviors, the timescale of the relationship between noradrenergic signaling and social (maternal) behavior is unknown since previous studies have relied on a mix of loss-of-function type approaches (e.g. knocking out the enzyme required to synthesize norepinephrine) and temporally imprecise recordings of noradrenergic activity (e.g. measuring release of noradrenaline while an animal engages in behavior). In this paper, the authors overcome these limitations of previous studies on maternal behavior by employing temporally precise recordings of activity of noradrenergic neurons.

In this article, the authors use a combination of electrophysiology and fiber photometry to evaluate the temporal relationship between firing of noradrenergic neurons in the locus coeruleus (LC-NA) and the stereotyped mouse female social behavior of gathering dispersed pups and bringing them back to the nest. Their major goals are to demonstrate that: 1) there is a phasic LC-NA response closely time-linked to pup retrieval; 2) this LC response is robust over time; 3) this response is not experience-dependent (i.e. present at full-strength upon first retrieval); 4) this response is linked to this specific behavior, and cannot be replicated by other similar types of behaviors (e.g. digging, retrieving a toy mouse, or receiving a food reward); 5) this neuronal response immediately precedes the behavioral output; and 6) LC-NA activity is correlated with locomotion speed only during pup retrieval.

The authors clearly succeed in providing sufficient data to support most of these conclusions, and the major success of this paper is using multiple orthogonal approaches to demonstrate the same, robust response.

The major weakness of this paper is a lack of sufficient context and framing, especially in the introduction and discussion. There are also a few technical concerns related to data presentation and statistics. We think these are easily addressable concerns, and ones that will demonstrably strengthen the significance of the paper, especially to a wider audience.

MAJOR CONCERNS

Technical:<br /> - Figure 6 uses log firing rates to quantify responses in some panels of the figure, while using z-scores in the other. This is concerning as the authors attempt to note differences in the results acquired by these two distinct techniques (ephys vs. fiber-photometry), with e.g. response of female’s LC to licking/grooming pup. Why not compare z-scores across both? If the authors wish to present data using both outputs, they should provide reasoning for the use of each metric and the benefits and drawbacks of each.<br /> - The authors should report the actual p-values of their statistical tests even if they are ‘significant’.<br /> - T-tests work under the assumption that your data is normally distributed. The authors should either confirm that their data is normally distributed or use a non-parametric statistical test that does not rely on such assumptions.

Context/framing:<br /> - The authors comment on a few possible points of significance in this study, including: 1) the link between phasic LC-NA activity and *social* behavior (highlighted in introduction); 2) the timing of phasic LC-NA activity related to behavior (highlighted in discussion); and 3) the uniform response of the LC associated with pup retrieval, which is a possible rebuke to the concept of a sub-specialized LC (highlighted in discussion). To enhance readability, we would encourage the authors to: 1) highlight the same point(s) of significance between introduction and discussion, and 2) spend a few more sentences in the introduction and, especially the discussion, really deliberately laying out ‘why this study matters’ to a generic neuroscientist.

MINOR CONCERNS

• The locus coeruleus is a pontine, not midbrain, nucleus
• What does half-max width of the PSTH mean, conceptually? Why is this a meaningful output measurement? Providing a brief textual description in manuscript or Figure 1 legend would enhance readability.
• Similarly, we would also welcome a brief textual description/explanation of the reasoning behind and methodological detail relating to the Z-score firing rate and the circular permutation analysis in Figure 2
• Why are all the means not centered around zero in the z-score scatter plots?
• Figure 3 looks at change in activity across days but not across individual trials. However, the heatmaps in figure 3 for P0 indicate that there may be some attenuation and temporal shift in the peak of the signal. It would be interesting to note whether this is consistent across animals as it would indicate that there is indeed change in the LC-NA responses across retrievals which would contradict the author’s current conclusions.
• For the electrophysiology experiments, the authors use each individually recorded unit as an independent sample. While their results are robust, they should potentially consider the use of nested statistics as this would be the proper statistical technique.

On 2023-02-03 15:15:36, user Ruili Xie wrote:

The article was accepted for publication on Neuroscience:

https://www.sciencedirect.c...

On 2024-11-19 15:22:35, user eggersii wrote:

I just want to make the authors aware that the lyophilized Hb product from Sigma is 'predominantly methemoglobin' (Similar to their bovine Hb product, ~99% metHb). This is a big distinction toward the Hb in blood cells/in vivo, which has only 1-2% metHb, with the majority still ferrous Hb (oxyHb/deoxyHb). While both ferric/ferrous Hb can react with H2O2, different products and different kinetics are at play. Thus, it is important to clarify which Hb state is actually used and tested.

Useful references:<br /> https://doi.org/10.1089/ars.2009.2826 <br /> https://doi.org/10.1179/135100003225002817 <br /> https://doi.org/10.1016/j.abb.2011.09.006

On 2018-10-11 08:40:12, user Guillaume Rousselet wrote:

It's really difficult to understand the results without proper illustrations of the effects. You do show some scatterplots, but this should be done for all comparisons given the small sample size. In particular, p<0.05 results cannot be interpreted without information about the distributions given that you used non-robust statistics. Here are some pointers:<br /> https://onlinelibrary.wiley...

On 2019-10-29 21:07:01, user Frank Hirth wrote:

In contrast to the outdated conclusion of Urbach 2007, new data on Pax2 (shaven), FGF8-like and Engrailed as well as dachshund and several other genes of the AP and DV axis specification unambiguously identify the deutocerebral-tritocerebral boundary (DTB) as the arthropod equivalent of the vertebrate midbrain hindbrain boundary (MHB).

On 2018-02-21 09:02:25, user Guillaume Rousselet wrote:

Interesting large dataset. Are the data available on a third party repository?

To compare correlations, you should consider a percentile bootstrap approach - see for instance:<br /> https://garstats.wordpress....

Non-parametric tests and the t-test do not assess the same hypotheses, so you should clarify the inferences you're trying to make.

Testing for normality is a bad idea: such tests lack power, so a null result is never conclusive. Better to illustrate the marginal distributions, for instance using histograms or kernel density estimates, and to measure effect sizes using robust techniques.

On 2020-11-23 08:15:35, user Yan Zhou wrote:

This study was well-designed and comprehensively investigated the role of LPO NMDA receptors in sleep. The finding of a NMDA receptor-involved pathology behind sleep-wake fragmentation might indicate potential treatments for insomnia by targeting these receptors. The paper’s structure is logical and easy to follow, from NMDA receptors’ cellular functions to behavioral functions and from general NMDA receptor deletion to cell-type specific knockdown of GABAergic and glutamatergic LPO neurons. One suggestion I have for the selective knockdown is to provide more background on the current knowledge of these neurons’ functions and why they were chosen in this study.

The data of this paper are very detailed and impressive, but it might be better if some figures can be rearranged to improve comprehensibility. Currently, each major figure consists of multiple, some with more than 10, subfigures, so readers might have to frequently refer to the captions, which could interrupt the interpretation of main ideas. I suggest maybe putting some subfigures, such as the design of DNA constructs and locations of viral injection, to the supplementary figures. For Figure 2B, it might be easier to understand the effect of GluN1 deletion on NMDA receptors, if the EPSC signals (with and without GluN1 deletion) of the same type of receptor are grouped together. Another suggestion is, compared to using left and right or the order of panels to describe subfigures, like in Figure 3D’s caption, it might be clearer to use a combination of letter and number, such as D1-4, to label them.

On 2020-04-05 14:44:19, user Damián Dellavale wrote:

Preprint accepted for publication in Clinical Neurophysiology on 05 April 2020, with minor changes.

On 2020-12-16 05:50:38, user LUC LORAIN wrote:

I really enjoyed reading this paper! Your characterization of the ASH1L phenotype was really striking, and seems really compelling from an outsider perspective. Considering the high rate of neonatal mortality in your knock out model, I was left questioning the prevalence of this mutation in the general public. After some preliminary research, it appears as though the ASH1L gene is primarily documented with regard to intellectual disability and behavioral phenotypes-- out of curiosity, have you come across any indication of abnormal development or greater neonatal fatalities in human surveys?

I think the brain histological staining was particularly well done, and clearly visualized the story this paper was selling. Your Figure 2B did a great job of demonstrating disruptions in the lamination process, with resolution and fluorescence-- clearly the result of a lot of experience and care!

In your Figure 4, the layout of some of your figures somewhat obscures the points your legends are trying to make. In the 4A heatmap, the chosen colors and arrangement of data points across multiple columns makes it difficult to see where there is actual differential expression. Perhaps in future work you could include arrows to highlight regions of interest, as well as using a color index with more striking contrast in the neutral ranges (green --> black --> red?). In 4B, I see the the point you are illustrating, but believe that the data could be strengthened by emphasizing that these GO terms are downregulated in the KO in comparison to the WT. Finally, I know that you include immunostaining results for some elements of the cytoskeleton in your supplemental figure S3D/E, but was wondering if it could be useful to stain for actin as another control. I would be interested to hear if other structural genes were considered, and what led you toTUJI1 and GFAP in the end.

Thank you for your contributions to public science, and good luck with future research!

On 2019-07-30 05:21:33, user Elisabeth Bik wrote:

I have some serious concerns about this paper. This animal model, in which mice receive 8 weeks of unpredictable stressors, such as 45% cage tilt, wet bedding, foreign urine in their cage, or confinement, does not appear to be mimicking human depression. Rather, it might induce extreme anxiety or PTSD.

These treatments lasted 8 weeks for the mice, which, assuming a 2y lifespan for a mouse and an 80y lifespan for a human, would be the equivalent of 6 years of borderline-torturing for a human. That surely would leave deep psychological scars and anxiety in these mice, but I fail to see how this could be comparable to human depression.

Human depression often does not have a clear cause and can happen to people who - from the outside - have a happy life. <br /> I doubt that most cases of human depression are caused by someone putting the patients' house at an angle, flooding their carpets, tying them up in a straitjacket, or urinating in their beds at unpredictable times for 6 years.

One could argue that there is some merit in treating mice with such horrible conditions in order to treat psychiatric problems of human patients who have been tortured, but I fail to see how this research could benefit humans who suffer from depression.

On 2022-02-13 09:33:47, user João Duarte wrote:

Published version in peer-reviewed Brain Topography, here: https://link.springer.com/a...

On 2025-11-05 20:48:15, user Matteo Gianeselli wrote:

This science is exciting and the aesthetic and color of the figures is just so pleasant to see!

On 2020-11-04 13:40:32, user Ed K wrote:

This reference might be relevant, since it compares the information transmitting capacities of ON vs OFF LGN cells: Pregowska, A., Wajnryb, E. Casti, A., Kaplan, E. & Szczepanski, J. (2019). Information processing in the LGN: a comparison of neural codes and cell types, Biol. Cyber. 113:453–464.

On 2022-01-26 03:17:21, user Danny Lustberg wrote:

If anyone wants the video file, let me know!

On 2021-01-05 04:10:24, user Claude La Due wrote:

NO. I do not believe this at all as it does not make ANY sense. My dreams have sound (though more often in the last dream segment) - so auditory cortex (even if imaginary), vestibular dynamics (most common, including with integrated imaginary physicality and sense of movement), somatosensory, and most importantly cerebral (thought) which is left out completely. Additionally, I can see hynagogia with eyes OPEN near my sleep cycle. Why so many new theories about "why" we dream? Why so much misinformation? The idea of parts of the brain naturally taking over others in the way claimed (with so much speed) sounds absurd.

On 2019-08-21 14:03:19, user Dityatev Lab wrote:

A very elegant work! Hopefully, in vivo correction of beta3 integrin haplo-insufficiency will follow...

On 2025-01-05 15:08:31, user PascualMarquiRD wrote:

Zero-lag connectivity for coherence and phase synch measures, obtained from EEG/MEG imaging, are definitely mostly determined by common source strength artifact, and have only a very small contribution from true physiological instantaneous connectivity.<br /> The inclusion of estimators of zero-lag connectivity can certainly "improve diagnostics and prognostics of <br /> brain-based conditions" because they convey physiological information, but it is hardly related to physiological connectivity, rather to activity.<br /> This assertion is based on realistic physics based calculations of false instantaneous connectivity from EEG imaging of cortical signals in the total absence of instantaneous connections

On 2023-07-10 12:02:50, user Vikram Vijayan wrote:

The published version of this preprint can be found here: https://www.nature.com/arti...

On 2018-01-11 23:16:17, user Leslie Vosshall wrote:

We received some great questions and feedback from Christopher Potter at Johns Hopkins. Emily Dennis's replies are interleaved below:

We just read your C elegans pre-print paper for our lab’s journal club. It was very interesting! I really liked the mutant screen. Very cool. We had a couple question/comments to send on (which I hope is OK?).

1. The work’s impact might be greater if you could test more thoroughly if str-217 was indeed a GPCR that responds to DEET. The HEK heterologous expression didn’t work, but can you instead express str-217 in another worm chemosensory neuron that doesn’t respond to DEET and see if that now confers a response? I’m not a C.elegans person, but it seems like this should be fairly easy to do (especially with the Bargeman lab nearby).

RESPONSE: I'm very excited about this experiment! We're doing our best to get clean signal/expression in a completely DEET-insensitive neuron (the first few neurons we looked at are affected by DEET in some way even without the str-217 receptor) -- we don't have this yet but those data will definitely make it into the revision(s) when we have them.

1. For the experiments testing if DEET could act as an odorant (Figure 1C), DEET appeared not to do much. But given your later results that DEET responses, and ADL neuron activity, lead to changed in search (?) behaviors, I’m wondering if maybe its worth taking a closer look? Maybe I read it wrong, but it sounds like a paralytic is added close to the odor source to make it easier to count worms that made an odor choice. But this might hide a DEET response? Can you instead track the behavior of worms as they get closer to the DEET source? It could be the DEET-in-agarose worked because they just needed a higher local concentration of DEET, meaning that you might only see an olfactory effect when they get quiet close to a DEET source.

RESPONSE: I totally agree it's hard to say DEET does nothing. We have had a really hard time coming up with a perfect experiment that separates the effects of method of delivery, time/duration of exposure, proximity, and concentration in these population chemotaxis assays. I did try a few things that didn't make it into the final paper that may be of interest. First, I added DEET to the lid of the dish and didn't see any effect of DEET (though we didn't include those data in the paper as the assay itself is non standard and a little messy since DEET can chemically interact with/'melt' plastic). I also did do some population chemotaxis experiments without the paralytic, and they look very similar to the results in our pre-print. Another related anecdote: in experiments with isoamyl alcohol and DEET as point stimuli, I often saw animals on the DEET spot (!) and the odor spots, but it would be fun to see if changing the distances between DEET/odor spots would change this, or if adding a DEET spot to a 'random' place on the plate would reveal any avoidance of that spot. In an experiment somewhat indirectly related to this idea of delivery/distance being important, I am also currently exploring how duration and strength of stimulation of ADL neurons specifically alters behavior (using optogenetics).

My intuition from observing these experiments is that DEET alone has very little effect as an olfactory/point stimulus in this assay. However, I definitely do not think we've fully explored all contexts that volatile DEET could interact with, so it would be interesting to go through, say, a larger panel of odor stimuli and co-present with DEET to see if there's any change or to add volatile/point sources of DEET to other assays and see what happens.

1. It wasn’t mentioned, but did the other mutants you identified also work in the same neuron, or perhaps implicate a shared signaling pathway?

RESPONSE: We only were able to map one other strain, which mapped to the gene nstp-3. Our early attempts to do cell ID and figure out where this is expressed weren't informative so we don't know if it's the same or different cells. My guess is there are lots of genes and lots of neurons required for complete DEET-sensitivity, so there's lots more to do & explore! I would love to see someone do a sensitized screen in the str-217 mutant strain to see if we can get even higher chemotaxis...

Nice work! Fingers crossed for a painless journal review.

RESPONSE: Thanks again, this was a lovely email to receive.

On 2020-11-24 15:05:53, user Jellina Prinsen wrote:

The peer-reviewed version of this work (open access) can be accessed at the publisher's website via the following link: https://rdcu.be/cbeWq

On 2024-08-07 04:43:36, user Kim Gruver wrote:

Now published in Nature Communications:

Gruver, K.M., Jiao, J.W.Y., Fields, E. et al. Structured connectivity in the output of the cerebellar cortex. Nat Commun 15, 5563 (2024). https://doi.org/10.1038/s41467-024-49339-1

On 2020-01-22 12:37:56, user James Mathew wrote:

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

On 2023-10-15 07:34:48, user Ben Dickie wrote:

Hi,

Really nice work. Please consider reporting your DCE methods using the new OSIPI lexicon to improve standardization. https://onlinelibrary.wiley.... I’m very happy to help integrate the correct terminology (ben.dickie@manchester.ac.uk).

Best wishes,

Ben

On 2019-09-03 21:20:16, user Antonio Pinto-Duarte wrote:

It is great to see these three new studies - by our team, the Goshen lab and the Hirase lab, demonstrating independently the critical contribution of astrocytes to remote memory: <br /> https://onlinelibrary.wiley...<br /> https://www.biorxiv.org/con...<br /> https://www.biorxiv.org/con...

On 2024-07-15 21:48:11, user Nipun Perera wrote:

This manuscript is now published in The Journal of Neuroscience. https://www.jneurosci.org/content/43/50/8649

On 2019-03-20 11:00:51, user Omer Faruk Gulban wrote:

Hi,

Congratulations for the paper, I have enjoyed reading it. I have 2 minor questions:

1- Would it be possible to see the supplementary figures? I am interested in the supp. fig. 3 from Resolution results section for seeing the accuracy around 200-400 micron range. This information would help me to determine the required image resolution in a project.<br /> 2- In Volumetric data preparation section, a smoothing method is mentioned (Kimia and Siddiqi 1996). I was wondering which software is used to apply this smoothing. I have also checked Wagstyl et al. 2018a too but couldn't find this information there neither.

Kind regards,

Omer Faruk Gulban<br /> (ORCID)

On 2018-01-10 08:24:43, user Philipp Berens wrote:

Great paper! This is very much in line with what we found in monkeys: http://www.jneurosci.org/co...

On 2017-02-17 16:37:50, user Adrien Peyrache wrote:

Very interesting article, we just discussed it in lab meeting. Views&remarks by Guillaume Viejo who presented the paper http://www.peyrachelab.com/...

On 2024-12-20 22:10:13, user Jesper Sjöström wrote:

This study has now been published in The Innovation:<br /> https://www.cell.com/the-innovation/fulltext/S2666-6758(24)00173-5

On 2022-06-21 06:16:50, user Andreas Papassotiropoulos wrote:

A truly elegant piece of work on the role of KIBRA in learning and memory. Congratulations to the team on combining evidence from rodent and human research and on interpreting GWAS data.

On 2024-03-18 09:19:52, user Mykhailo wrote:

Thanks for this interesting study. It would be beneficial for the paper to also discuss and compare to previous reports on profiling and mapping glia limitans: https://doi.org/10.1126/sci... and https://doi.org/10.1038/s41...

On 2016-09-26 16:31:11, user Finn Årup Nielsen wrote:

Wikidata item https://www.wikidata.org/wi...

On 2025-08-26 09:26:21, user Constant VINATIER wrote:

Feedbacks about your preprint : https://doi.org/10.1101/2025.07.24.666513

About registration: <br /> We could not find any information about the pre-registration of your study in the pre-print. Pre-registration involves documenting the hypotheses, methods, and/or analyses of a scientific study prior to its conduct (10.1073/pnas.1708274114; 10.1038/s41562-021-01269-4). If your study was pre-registered, we strongly encourage you to include the registration number in the pre-print, ideally in the abstract make this important information easy to retrieve, as this practice enhances transparency and reproducibility. If the study was not pre-registered, this should be acknowledged as a limitation. For future studies, we recommend pre-registering on an appropriate repository.<br /> About Protocol Sharing: <br /> We did not find the protocol for your study. If you have one, we encourage you to share it as supplementary material or deposit it in a publicly available repository such as the Open Science Framework ( https://osf.io ) or Zenodo ( https://zenodo.org/) "https://zenodo.org/)") . You can then include a statement in the Methods section indicating that your protocol is openly available (e.g., 'The protocol for this study is available at (link)/ in the supplementary'). Sharing your protocol will help readers better understand your study and enable them to reproduce it if they wish to test it.<br /> About the Statistical Analysis Plan Sharing: <br /> We did not find the Statistical Analysis Plan (SAP) for your study. If you have one, we encourage you to share it as supplementary material or deposit it in a repository such as the Open Science Framework ( https://osf.io ). You can then include a statement in the Methods section indicating that your protocol is openly available (e.g., 'The SAP for this study is available at (link)/ in the supplementary'). Sharing your SAP will help readers better understand your study and enable them to reproduce it if they wish to test it.<br /> About Deviations and/or changes<br /> We could not find any information about potential deviations or changes to the protocol in your pre-print. Since such deviations are common, if this applies to your study, we strongly encourage you to include a subsection titled Changes to the Initial Protocol in the Methods' section and discuss these changes as a potential limitation of your results. If any deviations occurred during your study, please specify them in this new subsection.<br /> About Data sharing / FAIR Data<br /> We found insufficient information about your data sharing approach. Data should be findable, i.e. data are to assigned a globally unique and persistent identifier (for instance there is a DOI assigned to the dataset, or data are registered or indexed in a searchable resource). Data should also be accessible, i.e. data are retrievable by their identifier and can be accessed following an open, free, and universally implementable protocol. As your data id not sensitive data, we encourage you to share it openly on a data sharing repository (Dryad, etc.) and include the Digital Object Identifier (DOI) in the Methods section. If you want more information about good practices of data sharing, visit https://www.go-fair.org/ <br /> About Code sharing<br /> We could not find any information about your (statistical) code. Sharing code is important for enhancing transparency and reproducibility, especially since it does not contain sensitive information. We encourage you to openly share it on a code sharing platform (Github, Codepen, CodShare, etc.) and include the Digital Object Identifier (DOI) in the Methods section. If you want more information about Code sharing https://fair-software.nl/ .

On 2019-05-14 11:02:19, user Lionel Barnett wrote:

I believe there is a problem regarding identification of the "ci" measure with Granger causality (and by implication transfer entropy), in the case where the "disconnection" corresponds to a Granger causality-type null hypothesis.

Specifically, according to my analysis (see link below), this identification will only hold in general when the residuals correlation of the full VAR model vanishes (or in the trivial case where the Granger causality itself vanishes). This conclusion extends to the spectral case.

Of course this by no means invalidates ci as an interesting and elegant measure in its own right, but I do believe it raises some issues of interpretation.

Please see my detailed analysis here.

Lionel Barnett<br /> Research Fellow<br /> Dept. of Informatics<br /> University of Sussex<br /> BN1 9QJ<br /> UK

On 2019-09-23 09:50:44, user Søren Grubb wrote:

Dear Erdener et al., thank you for sharing your nice paper!

I was wondering whether, with your OCT method, you can track the capillary stalls back to the lowest order capillaries? The reason I'm asking, is because we sometimes see blood cells stalling at the precapillary sphincter (see our preprint: https://www.biorxiv.org/con..., [supplements/657486_file07.avi]). If the lowest order capillary is stalled, it will affect many of the downstream higher order capillaries. Which means that it can possibly be few low order capillaries that are responsible for a lot of high order capillary stalls. Also, have you tried to dye the leukocytes to confirm that it is neutrophils stalling the capillaries in the two-photon videos and not platelet clots (as supplementary video 5, video 1 may suggest)? Neutrophils are known to affect platelet clotting and platelets can affect neutrophil activation, which would perhaps be less after you have depleted neutrophils with the LY-6G antibody.

Best regards

Søren Grubb, Lauritzen lab, Department of Neuroscience, University of Copenhagen

On 2021-06-22 17:28:05, user wsossin wrote:

This paper is now published.<br /> Neuroscience. 2021 Jun 15;465:128-141. doi: 10.1016/j.neuroscience.2021.04.023. Epub 2021 May 2.<br /> PMID: 33951504

On 2018-07-28 16:59:20, user Mary E. Hatten wrote:

Great paper, really exciting, but you omitted ref for Carla Shatz's classic discovery of Ca++ waves re spontaneous activity, Meister, Wong, Baylor and Shatz, Science 1991.

On 2023-03-27 15:47:46, user Ephraim F Trakhtenberg wrote:

Accepted for publication at Development https://doi.org/10.1242/dev...

On 2018-05-16 01:31:25, user Alexandre Magno wrote:

That's great, congratulations

On 2016-12-06 17:10:46, user Otto Fajardo wrote:

Hi. Very interesting work! What Matlab toolboxes are required to run Kilosort?

On 2020-09-19 09:29:28, user Raphael Goutaudier wrote:

This work is now improved and published in PlosOne ! Enjoy your reading !

On 2025-11-10 11:05:31, user Ariunjargal NyamErdene wrote:

This preprint has been published in the journal Biomaterials. Please find the direct link below.”<br /> https://doi.org/10.1016/j.biomaterials.2025.123838

On 2021-11-25 21:45:20, user Michael Ailion wrote:

This manuscript investigates how Gq signaling regulates egg-laying behavior in C. elegans by determining the relative contributions and sites of action of the two main branches of Gq signaling, PLCbeta and Trio RhoGEF. Mutants in PLCbeta and Trio RhoGEF are examined for steady-state egg-retention, vulval muscle calcium transients, and egg-laying in response to serotonin, optogenetic stimulation of the HSN motor neurons, or phorbol esters. Experiments are solid and performed rigorously, with appropriate sample sizes, statistics, and data presentation. The major conclusions are that PLCbeta acts exclusively in neurons, that Trio acts in both neurons and muscles, and that both pathways promote egg-laying by production of the second messenger diacylglycerol (DAG). Finally, it is concluded that DAG promotes egg-laying independently of the known DAG effectors UNC-13 and PKC. There is strong support for the conclusions that PLCbeta acts in neurons and that Trio RhoGEF has a major role in the vulval muscles. Support for other conclusions is somewhat weaker, and it would be good to provide stronger evidence or to tone down the conclusions. The title of the paper should also be changed accordingly.

Major points:

1. The conclusion that both PLCbeta and Trio RhoGEF pathways promote egg-laying by production of DAG is based on the clear rescue of these mutants by the phorbol ester PMA, a DAG-mimetic. However, it is possible that PMA is a dirty drug and has other non-DAG related targets, a possibility supported by the fact that loss of known DAG targets does not block the effect of PMA. Thus, these caveats should be discussed and this conclusion should be toned down.

2. It is concluded that DAG promotes egg-laying independently of known DAG targets UNC-13 and PKC. This conclusion is based on the fact that the phorbol ester PMA still stimulates egg-laying in single mutants of unc-13 or individual PKC genes. In addition to the possibility that PMA acts via a DAG-independent mechanism (see point 1 above), there are other caveats to this interpretation. There are multiple PKC genes that may be redundant or PMA may act via both UNC-13 and PKC genes (unc-13; pkc double mutants were not tested). Thus, these caveats should be discussed and this conclusion should be toned down.

3. The conclusion that unc-73/Trio RhoGEF acts in neurons in addition to muscle is based only on the quite weak rescue of the unc-73 mutant steady-state egg-laying defect by pan-neuronal expression of unc-73 (Figure 1K). One concern is that the unc-73 mutant (the control) had a considerably stronger defect in this experiment than in other data presented in the paper (Fig. 1I), raising the possibility that the weak rescue is just intrinsic variability in the behavior. Also, given that this rescue involves overexpression from a multicopy array, it is possible that there is leaky expression in other tissues such as muscle which would confound the interpretation. It is also implied that there is stronger rescue by coexpression of unc-73 in neurons and muscle than in muscle alone (Fig. 1K), but these two groups are not compared for statistical significance. Without stronger evidence that unc-73 acts in neurons, the conclusion should be toned down.

4. It is unclear why the egg-laying defect of egl-8/PLCbeta mutants is rescued by optogenetic stimulation of HSN neurons but not by serotonin – this is an interesting but weird result. A model is proposed (lines 488-506 in the Discussion), but it is quite confusing and hard to follow – a model figure might help. It seems that the authors propose that PLCbeta may act outside of the HSNs to promote egg-laying, perhaps in the VC neurons. However, it has not been demonstrated that pan-neuronal rescue of egl-8 is able to rescue its serotonin-resistant phenotype and that muscle-specific expression fails to rescue, which are important experiments to conclude that egl-8/PLCbeta is indeed acting in neurons. It would also be interesting to see what happens with HSN and VC-specific rescue.

5. Related to point 4 above, in several places it is stated that PLCbeta and Trio RhoGEF act presynaptically (lines 84, 485-487). Given that it is unclear which specific neurons these pathways act in, it is unclear whether they are indeed “presynaptic.” We suggest avoiding the terms “presynaptic” and “postsynaptic” and instead say “neuron” and “muscle.”

Minor points:

1. In Fig 1J, it appears that pan-neuronal rescue of egl-8/PLCbeta actually causes hyperactive egg-laying. It is unclear whether this is due to overexpression or possible ectopic expression, which would have caveats for inferring a neuronal site of action of endogenous egl-8/PLCbeta based on the use of this transgene.

2. line 337-338: In data presented in this paper, an egl-8(sa47) mutant is reported to be resistant to serotonin, whereas it was previously reported to be sensitive (Bastiani et al. 2003). It is suggested that the discrepancy may be due to different levels of serotonin. However, given that the previous paper used a lower concentration of serotonin (7.5 mM instead of 18.5 mM), this explanation does not make sense. Additionally, Bastiani et al. reported that an unc-13(e51) mutant laid eggs in response to serotonin, again inconsistent with the results of the current paper where unc-13 is shown to be resistant. This discrepancy should also be pointed out, even if it cannot be explained. The previous paper also examined a third allele of egl-8, md1971, and found it to be sensitive to serotonin. A reexamination of that mutant would also be useful.

3. line 508: it is stated that Trio RhoGEF acts in motor neurons to control locomotion behavior. However, Hu et al. 2011 only showed rescue of unc-73/Trio RhoGEF mutants by expression in widely expressed neuronal promoters (unc-119 or egl-3 promoters), and did not observe rescue by motor-neuron specific promoters (unc-17 and unc-47 promoters).

4. For the calcium imaging experiments in Figures 4 & 5, it is unclear how frequency of calcium transients is measured. Is this the frequency of transients during the entire recording or only during active egg-laying states? If it is during the entire recording, how to control for different numbers of active states from animal to animal? In the trace shown in Fig 4A, a dgk-1 mutant appears to have a higher frequency of transients than wild-type outside the active state as well as an increased amplitude of egg-laying transients – is this trace not representative?

5. Two pkc-1 mutants were stimulated by PMA but one wasn’t (Fig 6D). This discrepancy is not discussed.

6. line 312: It is stated that rescue of unc-73 by expression from a muscle-specific promoter is greater than from a neuron-specific promoter. Though this appears to be true in the data presented, this conclusion is based on comparing rescue by different transgenes; given that there is often variability in rescue even by different transgenes of the same construct, this is a tricky comparison to make. All of these rescue lines are overexpressed and could be overexpressed to different levels or with different amounts of mosaicism, either of which could affect the level of rescue.

7. line 464: “To determine whether serotonin promotes egg laying through Protein Kinase C…” – however, the following experiment does not actually test pkc mutants for their serotonin response.

8. line 284: it is stated that wild-type worm retain 12 eggs, yet in Fig 1C, it says 15. In line 330, it states that 70% of HSN-deficient worms laid eggs in response to serotonin, but the data in Fig 2B shows slightly under 60%.

9. typos: line 367 – should be egl-8(n488); line 430 – should be Fig. 5B.

Reviewed (and signed) by Amy Clippinger and Michael Ailion

On 2022-11-02 07:48:23, user Angie Renton wrote:

This is a great and much needed review, I look forward to seeing it published! Thanks for including my recently published EEG neurofeedback paper (Renton et al, 2021). Just letting you know that you've missreported the effect for key behavioural measure of this study as non-significant - we did find significant neurofeedback training effects on both our neural and behavioural outcome measures. This is an easy mistake to make because of the design of the study and the way the results are reported in the figures. Please let me know if you have any questions about this or about the study. Hopefully this finds you early enough in the review process to correct the info :)

On 2017-11-23 03:05:05, user jvkohl wrote:

I don't think the moderator will allow discussion of the link from (NAD+) to RNA-mediated DNA repair, because it links food energy-dependent changes in the microRNA/messenger RNA balance from the pheromone-controlled physiology of reproduction to the difference between C. elegans and P. pacificus -- a predatory nematode with teeth. That fact refutes the pseudoscientific nonsense about mutations and "evolved" biodiversity.

On 2018-06-15 09:43:27, user Remi Gau wrote:

A bit of a late comment but is there a reason why figure 4 should not be turned into a comination of a forest-plot and a funnel plot?

https://en.wikipedia.org/wi...<br /> https://en.wikipedia.org/wi...

Not exactly an expert in meta-analysis methods but I think that funnel plots can be used to suggest publication bias which is linked to the the significance filter mentionned in the paper.

Some other tools can also be applied (p-curves, Z-curves...) to get more ou of this: many of them can be found here http://shinyapps.org/apps/p...

Hope it helps.

Remi

On 2018-01-05 21:51:01, user Chris Gorgolewski wrote:

cross-linking my open peer review: http://academickarma.org/re...

On 2019-05-15 23:54:40, user gwern wrote:

Is the difference between Gf and Gc still statistically-significant after correcting for the differing levels of measurement error in each? The NIH Toolbox has much greater error in measuring its 'fluid' construct than it does its 'crystallized' construct (eg Table 3 https://www.ncbi.nlm.nih.go... has Gc correlating .90 but Gf only .78 with their 'gold standard' tests), which alone would produce substantial differences in variance explained.

On 2021-10-31 21:19:09, user Arthur Zhao wrote:

Hi Yue Zhang, quick question, are the movies available ? I only see one pdf file in the supplementary material.

On 2025-07-17 18:28:35, user Molly McDougle wrote:

Beautiful studies and very fascinating implications! I did not see it listed anywhere how the drugs were administered, although I may have missed it... can you tell me if it was intraperitoneal (I assume)?

On 2016-04-05 06:18:51, user Chris Gorgolewski wrote:

One of the complaints I often hear when encouraging people to share code concerns providing user support. Researchers are worried that, by publishing their code they are obligated to provide time consuming user support and reply to countless emails describing installation issues and requests for new features. Many people don't share their code to avoid that. My response to such concerns is: build a user community around the code that will be able to help each other. It is as easy as setting up a Google Groups mailing list and clearly stating that all announcements and user support will be made though such list (NeuroStars.org could also be used for this). This takes the pressure from getting personal emails send directly to you, creates a publicly searchable database of user issues and helps users to help other users. We have described this solution in our recent paper: http://biorxiv.org/content/...

On 2016-09-23 13:36:42, user Daniel Hähnke wrote:

Very interesting paper! Do you have a video of the mouse on the wheel?

On 2017-09-08 20:52:36, user Hector Junior wrote:

Very nice and interesting paper. On a side note, one has to always be careful when applying Entropy as the following paper demonstrates: "Low-algorithmic-complexity entropy-deceiving graphs" (the results generalize to objects other than graphs, such as time series). Here the freely available paper URL: https://journals.aps.org/pr...

On 2024-03-13 09:31:14, user Stergios Tsartsalis wrote:

Now published in nature communications:<br /> https://www.nature.com/arti...

On 2021-09-17 09:57:13, user TELL wrote:

This<br /> article is interesting since the authors tried to unveil an<br /> interaction between Gaba inhibition and KV 4 conductance. However, I have several concerns (or misunderstanding).

First in the method, it is not clear how the authors measured<br /> their effects. Did they stimulate n times the TS with and without UV illumination and/or drug treatments, or only once ? In the latter case, as suggested by the use of a paired t-test, the expected variability from trial to trial may have obscured the outcome.

Second, the authors are surely aware that KV4 current are strongly inactivated at rest and that the window current amounts at best 10% of the full conductance (our work (Vincent and Tell, 1997; Strube et al., 2015). In addition, the recovery tau of inactivation is about 80 ms (from 20 to 140) at -100 mV (junction potential corrected (Strube et al., 2015).

This means that the full recovery needs a three times more duration. We may thus expect that Gaba Epsp may be too weak to de-inactivate sufficiently KV4 channels. On figure one, the inhibitory synaptic barrage hardly reach -5 mV for one second. Given that the resting potential is about -60 mV, the potential reached during inhibition would be -65 mV. It should come as a surprise that this tiny de- inactivation of KV4 was unseen. In addition, shifting the resting potential to more negative value would reduce the window current ((Strube et al., 2015). BTW, the authors did not observe a change in resting potential after blocking KV4 channels, arguing against a strong window current at rest. We know that EPSPs are certainly stronger at synaptic sites.<br /> According to the Nersnt’s equilibrium, the Ecl- is about – 90 mv<br /> but the amplitude of the observed EPSPs do not suggest that<br /> hyperpolarization to be such large at synaptic sites.

p { margin-bottom: 0.25cm; line-height: 115%; background: transparent }

On 2016-08-26 12:26:34, user Bronson B Harry wrote:

Although the recent spate of studies evaluating the fMRI literature are critical, the current discussion has so far missed the opportunity to assess different analysis approaches deployed in the field. Indeed, the debate over the reliability of fMRI methods has advanced rapidly advanced over the last 15 years (arguably more so than Psychology). However, this is not to say that the most rigorous methods have been widely adopted. It is critical that evaluations such as the one reported here point to the reliability of different methods so that clear recommendations can be made for the future of the field.

The current gold standard in fMRI research are studies using the functional localisation approach. Functional localisation studies deploy standardised tasks for reliably identifying regions of interest in individual participants. Typically, separate datasets are used to identify regions of interest and test experimental hypotheses. The benefits of functional localisation are numerous, and are discussed extensively in Saxe et al (2006) and Vul & Kanwisher (2010).

The prototypical example of the functional localisation approach is in the field of visual cognition and categorisation. Would it be possible to include a case study that assesses the reliability of studies examining three of the most widely studied functionally defined regions of interest; the Fusiform Face Area, the Parahippocampal Place Area and the Lateral Occipital Cortex? Most of these studies include the name of the region of interest in either the title or as a keyword and should be easy to find with a keyword search.

Although this literature is small in comparison to studies using exploratory, whole-brain approaches, focusing on this issue would speak to a broader ongoing debate in the literature about the ideal methods used in the field.

On 2016-08-26 17:11:46, user Jochen Weber wrote:

Could you please clarify how exactly your effect-size-extraction algorithm used the text of any given manuscript to calculate a standard effect size (i.e. which parameters and values were put into which formula/s), particularly given the following arguments?

As you state in the Introduction section, power estimates and NHST are somewhat mutilated constructs that came out of Fisher's and significance testing approach and the Nyman-Pearson theory. In most of the (current) neuroscience literature, significance is not merely assessed by the (raw) statistic, but instead a "combined height-and-size thresholding" procedure is applied.

This combined approach typically results in a "significant finding" if (and only if) a contiguous set of voxels (i.e. a cluster) in its entirety surpasses a "cluster-forming threshold" (uncorrected p-value, typically set between one-tailed p<0.01 and one-tailed p<0.001, including those boundary values) *AND* when the cluster at least is of a size (typically measured in number of voxels) that is determined via one of several ways, including Gaussian Random Fields theory (e.g. in SPM), simulating noise data with a separately estimated smoothness (e.g. in AFNI's AlphaSim), or via permutation testing (non-parametrically, e.g. in SnPM).

The relevant statistic to assess its "significance" (related to the likelihood of detection and power!), really, is then the p-value of a cluster (not its peak or average t-value). For example, if any given test (such as a two-sample t-test with group sizes N1=12 and N2=12, for a d.f. of 22) is performed (and a whole-brain search is reported, i.e. no spatial prior is used!), a typical manuscript would then contain a table of all clusters (and their peak coordinates and sizes) that reach this combined threshold, as an example, the authors may have chosen to apply a CDT of p<0.001 (t[d.f.=22] >3.505), and with an assumed voxel size of isotropic 3mm (27 cubic mm/voxel), and a smoothness estimate of 13.5mm, an application of the AlphaSim algorithm would lead to a required cluster size of approximately 73 voxels (i.e. if in a contiguous region of space spanning at least 73 3mm-cubed voxels inside the brain mask all voxels surpass a t-threshold of 3.505, this region would be considered "significantly different" between the groups).

Please then be aware that for this "activation difference", the p-value is still *just* 5% (i.e. the chance-level of finding such a cluster size given the t-threshold in noise conditions is 1 in 20). That being said, I think it is then equally fair to ACTUALLY compute the standardized effect size for such a cluster from a t-value of 1.717 (d.f.=22; one-tailed p<0.05).

In case your algorithm uses the (maximum or mean) t-value reported for the cluster, it is only natural that the standardized effect size distribution MUST be skewed towards a region that is not in line with traditional psychological research (given the massive application of family-wise-error correction procedures due to the mass-univariate testing approach, still dominant in the field).

As a summary prescription and recommendation for the literature/field, I would consider urging authors of future publications to always report cluster-level p-value estimates (which can be assessed in both GRF and simulation methods by comparing the observed cluster size of any cluster reaching significance with the distribution of cluster sizes under the NULL), such that effect sizes, at least when it comes to using them for the purpose of power estimates, can be computed appropriately.

Thank you so much for your consideration and efforts!

On 2020-06-20 11:28:41, user Tom Baden wrote:

Very interesting findings! One note, I suspect some differences you observe in the distribution of spectral responses compared to Zhou, Bear et al. 2020 can be linked to the stimulation from below, as also noted in the intro. For example, regarding the relative sparseness of UV responses: In nature, there is essentially no UV light that hits the dorsal retina, because the ground beneath the fish is poorly UV-reflective. Presumably as a result, the fish do not invest much into UV circuits in dorsal retina. See also Yoshimatsu et al. 2020.

On 2019-10-06 10:28:36, user Socratic Paradox wrote:

Goebrecht et al. (2014) [https://www.sciencedirect.c...] is not the only study to show TH-ir neurons in the ventral telencephalon and posterior tuberculum of midshipman, see also Forlano et al. (2014) [https://onlinelibrary.wiley...] and Ghahramani et al. (2015) [https://www.karger.com/Arti...]. The later study also correlates differences in brain catecholamine expression with divergence in reproductive strategies between midshipman male morphs, which is notably relevant to the discussion put forth by this new study in blind cavefish.

On 2018-02-23 10:04:31, user Michael Spencer wrote:

Presenting averages as bar plots is generally misleading (Figure 1) as they lead the reader to view the bar size as totally inclusive of the value. A better way to present average data is with a boxplot (or similar). However, in this case you have a Likert scale and it is bad practice to take an average of these (e.g. https://sciencing.com/avera... "https://sciencing.com/average-likert-scales-6181662.html)"). Perhaps a better way to present these data is as a stacked bar plot with colours representing number of responses in each group.

On 2018-02-13 21:42:50, user Yu-Chieh David Chen wrote:

This review was done as part of the Journal of Neuroscience reviewer mentoring program http://www.jneurosci.org/co.... (Mentor: Dr. Wayne Sossin, Menthe: Yu-Chieh David Chen ).

Oswald and colleagues use genetic, behavioral and imaging approaches to analyze the role of reactive oxygen species (ROS) in regulating activity-dependent structural plasticity in neuromuscular junctions (NMJs) of Drosophila larvae. By measuring synaptic bouton numbers, active zone size, and dendritic arborization of larval motor neurons, the authors revealed that both pre- and post-synaptic structural changes in response to increased neuronal activity. Such changes are important for the adaptive behavioral output as measured by larval crawling assay. For example, induction of prolonged activity by either shifting rearing temperature or dTrpA1 mediated heat activation in the larval motorneuron network leads to homeostatic adjustment of crawling speeds. They further suggested DJ-1 acts as a ROS sensor and mediates the neuronal structural changes partially through the PTEN/PI3Kinase signaling.

Synaptic plasticity is one of the fundamental mechanisms underlying brain function and behavioral adaptation. Understanding the molecular signaling that regulates synaptic plasticity provides an important step for understanding how changes in synaptic transmission are translated into behavioral changes. There are many signaling pathways that are involved in synaptic plasticity, such as kinases ERK, PKC, PKA and CaMKII and the phosphatases calcineurin, PP1 and PP2A. Many kinase proteins are shown to be activated at certain redox environment and the kinase activity can be regulated by the level of ROS, which in turn inducing changes in synaptic plasticity. However, the direct role of ROS in behavioral and neuronal plasticity has never been tested and this is a novel aspect of this current manuscript by Oswald and colleagues. However, the identification of DJ-1 as an underlying redox sensor for detecting ROS is not new since various oxidative stress conditions have been shown to induce DJ-1 expression and the role of DJ-1 in regulation of oxidative stress has been seen in both vertebrate and invertebrate.

Although the study by Oswald et al provides some insights into how ROS might be used to regulate synaptic structures in responses to neuronal activity, in its present form it falls short of delivering conclusive results. In particular, discrepancies between the effects of temperature shifting and dTrpA1 activation on behavioral adaptation and the NMJ ultrastructure question the major conclusions of the authors (see detailed comments below). There are also additional concerns about the conclusions that are not supported by their results (detailed in the major concerns). My major and minor concerns are stated below:

Major concerns:

1. In Fig. 1B, there is no control for the effect of dTrpA1 on crawling speed at 27oC. There is also no discussion of whether a normal acute or prolonged change from 27oC to 22oC causes a change in crawling speed in control animals.

2. Throughout the manuscript, the authors used “moderate or mild activation” and “over-activation” to explain the two modes of activity-regulated structural plasticity in many places of the manuscripts (e.g. line 148-153; line 236-240). However, it is not entirely clear how the authors define the “strength” of activation. Is it based on temperature used when expressing dTrpA1 in these motor neurons? If so, how related are the dTrpA1-meidated activation to the chemical or transgenes-mediated activation such as using paraquat, DEM, and UAS-Catalase, UAS-SOD2? How would the authors know whether these chemical or transgene-mediated ROS inductions fall into the range of “mild neuronal activation” or “stronger neuronal over-activation”? Also, the description of “over-activation” at page 15 line 254 is vague to readers.

3. Given the data presented in the manuscript, it is not clear whether behavioral adaptation in response to acute upshift/chronic adaptation/acute downshift occur due to changes in motoneurons. The discrepancies between the effects of temperature and the changes at the NMJ suggest that the important behavioral adaptation is not at the NMJ. For example, homeostatic changes in crawling speed are seen when animals are raised at 29 oC from 25oC (Fig. 1A). However, no ‘homeostatic’ change are seen at the NMJ (Fig. 2C, 2G); indeed, the only significant effect is an increase in active zone number (Fig. 2E). This seems to dissociate NMJ changes from the homeostatic change in behavior, suggesting that other elements of the circuit are important for the behavioral change observed. It also suggests that there are fundamental differences between the effects of dTrpA1 and temperature. The discrepancy between temperature mediated structural plasticity and dTrpA1-mediated structural plasticity also creates a gap between ultrastructural phenotypes and the behavioral crawling phenotypes since most of the ultrastructural phenotypes are observed by dTrpA1-meidated activation (Fig. 4) while most of the behavioral crawling phenotypes are observed by shifting temperature (Fig. 5). This is particularly important because all the data on ROS inhibition only is done in the TrpA1 activation background, not the temperature shifts. Thus, the conclusion that ROS inhibition is important for behavioral adaptation to temperature is not strongly supported.

4. I am surprised that the author did not observe a significant increase in ROS production through dTrpA1-mediated activation in aCC/RP2 neurons at 25oC (Fig. 3A). However, the ultrastructural changes can be seen at the same condition (Fig. 2C, 2E, 2G). Does that mean these ultrastructural changes are ROS-independent? How do the authors reconcile this discrepancy? Moreover, the data showing the role of DJ-1 acts as a ROS sensor to mediate activity-induced structural plasticity is done at 25 oC (Fig. 4A,B), if there is no increase of ROS at this temperature (Fig. 3A), there might be other sources of ROS production other than neuronal activation.

5. A major claim by the authors is that ROS is an important signaler of activity to homeostasis. However, the acute downshift after reducing temperature to 25 oC after rearing at 29 oC or 32oC is not significantly different in the presence of DJ-1C104A (the difference seen is most likely to the lack of an effect on chronic adaptation thus, the starting speed in the DJ-1C104A animals is presumably much faster (Fig. 5). This dissociation between chronic adaptation in basal speed and the effect of downshifting seems quite important and needs to be discussed. In this context, the lack of data on the chronic adaptation of DJ-1C104A at 29 oC needs to be fixed, since there are already differences in the effects of temperature at 29 oC and 32 oC, for example on bouton numbers (Fig. 2C) and active zone numbers (Fig. 2E) and their correlation with differences between downshift and chronic adaptation need to be detailed.

6. In the figure legend of Fig. 6A. The authors stated that ”Removal of one copy of PTEN sensitizes (red) while heterozygosity for DJ-1 desensitizes NMJs to ROS levels (yellow), partially restored in double heterozygotes (blue).” The description for double heterozygotes (blue line) is not entirely correct since the double heterozygotes might not be significant difference from heterozygote of DJ-1 (yellow). The authors should run statistical test to confirm the differences in this dataset.

Minor concerns:

1. It is not very clear that which group is being compared in Fig. 2C and Fig. 2E. I assumed the asterisks above each group in these two panels are showing significant different from the Oregon R 25 oC group.

2. The insets of the Fig. 2C,E,G, Fig. 3, Fig.4 and Fig. 6 should indicated in the figure legends.

3. At page 13 line 219-220. It will be good if the authors specify “paraquat” and “DEM” followed by the description of pharmacologically induced oxidative stress.

4. In Fig. 1A, there is a further decrease of crawling speed when acutely downshift temperature from 32 oC ->25 oC than 29 oC ->25 oC in wile type larvae. However, there is no such a further decrease in control flies (dVGlut-GAL4 only) in Fig. 5. Is this variation due to genetic background of the flies?

5. In the Fig. 7, I am not sure why the authors still put a “?” between DJ-1 and reduced arborisation and a “?” between DJ-1 and reduced active zone number. Didn’t the authors provide evidence to show DJ-1 mediates these two phenotypes in Fig. 4B and 4C?

6. At page 20 line 326-327, please clarify what the systematic manipulations indicate. Also in line 329, why the authors refer to Fig. 5. Could the authors clarify these points?

7. At page 22, line 392, it is not clear to me that on what basis do the authors come to the conclusion that the ROS is “sufficient” for activity regulated structural plasticity.

8. In Fig. 3B, the dTrpA1-meidated neuronal activation induces an increase in bouton numbers and this is partially through ROS. The data in Fig. 3B indicates that there are other factors that contribute to the increase in bouton numbers since the aCC/RP2-GAL4>UAS-Catalase, UAS-dTrpA1 flies still showed more bouton numbers than control. However, the other factors might also mediate the increased bouton numbers through PTEN since aCC/RP2-GAL4>UAS-PTEN, UAS-dTrpA1 flies abolished the increased bouton numbers compared to Control in Fig. 6B. Could the authors comment on this findings?

Typos/mistakes:

1. At page 3 line 78, I assume the author try to say “we used the motor system of fruit fly larva as a model…”. A similar mistake can be found at page 19 line 298.

2. There is a typo in figure legend of figure 1, change “chromic” to “chronic”.

3. At page 17 line 274, “15 mM” should be “10 mM”.

4. At page 27 line 525, “chord”-> “cord”

5. At page 27 line 543, “Mouse-nc82”-> “Mouse-anti-nc82”.

6. There is no in-text citation for fig.4-Figure supplement 2.

On 2023-03-27 07:31:04, user Sidney I. Wiener wrote:

This paper was published on 21 March 2023 in Frontiers in Cellular Neuroscience<br /> DOI 10.3389/fncel.2023.1131151

On 2017-07-13 19:35:56, user Gerard Rinkus, Ph.D wrote:

This is an interesting article describing specific functions for the neocortex's layers<br /> and for its macrocolumnar tiling. Its main idea is that multiple codes in an<br /> 'input' layer, e.g., L4, can become associated over time with a single, more<br /> temporally stable, code in an 'output' layer, e.g., L2/3, which, as they point<br /> out, conforms with Hubel & Wiesel's original concept of simple and complex<br /> cells. They also propose layer 6a and 5 as another possible instance of this input-output circuit. As they state, this association principle applies equally well, whether the sequences of active codes in an input field arise due to movements of objects in the world or to movements of the receptive field with respect to the world (e.g., eye saccades).

Readers (and actually, the authors too) might also be interested in another, quite similar,<br /> sparse distributed representation (SDR) based theory of sequence learning, recognition, recall, and inference, called Sparsey. As described in Rinkus (2014)(http://journal.frontiersin.... "http://journal.frontiersin.org/article/10.3389/fncom.2014.00160/full)"), Sparsey uses the same concept of associating sequences of SDRs in one field with longer-lasting ("persisting") codes in another field, but posits the two fields in question as being at different (adjacent) levels of the cortical hierarchy (as opposed to different layers at the same cortical<br /> level (region). The 2014 paper is based on earlier descriptions of the core SDR-based sequence memory model, e.g., Rinkus 1996 (http://www.sparsey.com/Rink... "http://www.sparsey.com/RinkusThesis.pdf)"), 2010 (http://journal.frontiersin.... "http://journal.frontiersin.org/article/10.3389/fnana.2010.00017/full)"). It seems plausible that both instantiations of the basic principle for achieving invariance, associating multiple, more transient codes, with single, more persistent codes (which essentially underlies Hubel & Wiesel's explanation) could be operative in the brain. It's important to realize that this kind of mechanism allows learning of essentially arbitrary invariances, which amongst other things, could include the kinds of invariances that have for decades been hard-wired, i.e., translation, rotation, and size invariance (i.e., log polar Fourier).

If above links don't work, they are all available at www.sparsey.com/Publication....

On 2025-11-17 10:08:13, user MARIANNA ANGIOLELLI wrote:

The manuscript ID#: BIORXIV/2024/603226 has been published in Physical Review Research. The new version of the manuscript, (Role of criticality in the structure-function relationships in the human brain) was published on the 12th of November. The DOI is: https://doi.org/10.1103/PhysRevResearch.7.043153 .

On 2024-06-13 18:59:03, user Miriam Goodman wrote:

Corresponding author here to note an error in Figure panels 1B and 1D. <br /> Figure 1B: The markings indicating the starting zone are misplaced. Readers seeking to make their own foam inserts are encouraged to contact the corresponding author for dimensional diagrams.<br /> Figure 1D: “Iodixonal” is mis-spelled. It should be “Iodixanol”

On 2019-06-09 09:53:20, user Ghislaine Dehaene wrote:

It is now published <br /> Kabdebon, C., & Dehaene-Lambertz, G. (2019). Symbolic labeling in 5-month-old human infants. PNAS, doi:10.1073/pnas.1809144116

On 2022-04-01 22:48:34, user Owen Hamill wrote:

An editorial on this article by Andreas Draguhn has been published:

https://www.imrpress.com/jo...

On 2022-04-19 15:46:36, user Owen Hamill wrote:

"A recent article by Suraiya Luecke on Freediving (https://doi.org/10.1007/s11... "https://doi.org/10.1007/s11097-022-09808-8)") has reported that freedivers do a relaxation breathe-up, typically involving several minutes of slow, diaphragmatic, and exhale-biased snorkel breathing, while floating face down on the surface of the water. This practice evokes a very specific set of neurological, physiological and psychological outcomes, which allows for the exceptional experience of freediving to depths as great as 214 meters. The fact that the breathe-up often involves mouth breathing with a snorkel adds support to the idea that an intrinsic resonance mechanism involving breathing-induced intracranial pressure (ICP) pulses transduced by PIEZO channels may promote promote the specific brain state. Once the dive commences, cardiac-induced ICP pulses transduced by PIEZO channels may further regulate in a top-down manner the reduction in freedivers' heartbeat rate (to as low as 12 beats/min).

On 2021-12-23 21:29:42, user Anonymous Peer Reviewer wrote:

This review was done as part of the SfN Reviewer Mentor Program (https://www.jneurosci.org/rmp) "https://www.jneurosci.org/rmp)").

Review of Paunov et al. https://www.biorxiv.org/con...

The authors identify that the study of language and theory of mind (ToM) processing has been conducted, for the most part, independently in laboratory settings, leaving questions for dependencies between these systems in more naturalistic settings. They ask: do language and ToM neural networks dissociate even in response to naturalistic stimuli, which have varying levels of ToM and language content?

General evaluation:<br /> Key strengths of this paper are: 1) the use of naturalistic stimuli that are rich with theory of mind and language content to study the functional dissociation between ToM-Language networks, and 2) the methodology – a combination of individual-specific functional localization and inter-subject correlation (ISC) – that minimizes reverse and non-specific inference based on anatomical locations only. Inclusion of resting state ISC to ensure that the task-based ISC are not the result of data acquisition, preprocessing, or modeling procedures add to their interpretation of results. That said, 1) nuanced discussions of conflicting key data in the context of their paradigms, and 2) additional whole-brain level ISC analyses given the limitations of using ISC in inferring dissociability of ToM and language networks could substantially increase the value of this paper to the field.

Major comments:<br /> 1. Please further qualify the interpretations of “functional dissociability” between language and ToM networks in the context of the posed question and used paradigm. A major conclusion from the experiment is that there was indeed dissociability between language and ToM neural networks, albeit incomplete, even under naturalistic stimuli rich with social contexts. However, as the authors reviewed thoroughly in the introduction and Figure 1, cognitive demands on ToM processing may come from heterogenous contexts, i.e. mental state inference and mental state content comprehension. Since their objective was to evaluate differential recruitment of language-ToM networks in processing the latter, and the ToM localizer task they used specified regions involved in comprehension of mental state content that are linguistically presented (Figure 2), their main ToM neural results supporting network dissociation need to be interpreted from that methodology. For example, can you use the ToM activity in condition “-Language +ToM” (where regions were selected by language-based ToM localizer task) as evidence for mental state content comprehension, when the stimuli themselves were presented non-linguistically? It could have instead represented the process of mental state inference, which is not what the authors aimed to measure. How do you interpret left TPJ, which exhibits weak but significantly above-baseline tracking of the expository text (“+Language -ToM”); could it be that the participants were inferring what these expository words mean using higher order processes, and that ISC is measuring this common internal process, even though there is no ToM content to track (the original purpose of this task)? Even though the authors discuss generally that naturalistic stimuli such as those used in this paradigm may require fundamentally increased combination of ToM and language network interaction as well as heterogenous ToM context processing (inference/comprehension), qualifying interpretations of currently conflicting neural data by returning to their originally stated research goal will add concrete insights into the nature of “functional dissociability” under real-life conditions.

1. Please provide results in the control regions that are not supposed to be involved in language or ToM across the modalities of stimuli presentation. Ideally, authors can also provide whole-brain maps of ISC during the tasks, as these would provide a more comprehensive picture of inter-network differences and similarities underlying critical ISC results, which are currently grounds for supporting ToM-language dissociation. As the authors acknowledge in Line 851-855 and 947-948, ISC can be driven by anything that is consistent across participants at the time of doing the tasks, not necessarily reflecting language or ToM-related processing (Nastase et al., SCAN 2019). For example, the ToM network overlaps with the default mode network, which becomes more active when participants are reflecting and directing attention internally. The stimuli may have had varying levels of engagingness and subjectively meaningful processing. In other words, with the current datasets we don’t really know if the domains that the authors were interested in drove ISC. By contrast, some regions that are more idiosyncratic (e.g. less stimulus driven) could have been crucially involved in processing ToM content, perhaps suggesting that lower ISC (e.g. RH PCC/precuneus in dialog condition) in fact was a false negative in that ROI.

2. Signal-to-noise-ratio may be stronger in certain ROIs than others, leading to differences in ISC. Please provide some correction or normalization of this to qualify the validity of the results comparing ROIs.

Minor comments:

1. If they have this information, in 2.3 Participants, another exclusionary criterion should be impairment of ToM or language network either from neurological or psychiatric conditions.

2. Authors found in reality check #1 experiment that these individuals’ auditory and visual cortex activity were also not completely dissociated and crossed over to track visual and auditory stimuli, respectively during active task performance. I would like to hear the interpretations of these results with regard to the main results.

3. Authors conducted multiple demand network analysis as part of the replication analysis – please (if it does) explain how this adds to their conclusion/results of this paper.

4. In Line 1043, be more specific how methodology enhanced with temporal resolutions (MEG, intracranial recording) could be better for investigating mechanism of ToM-language network interaction?

On 2025-06-19 07:35:18, user khaled ghandour wrote:

This is Khaled Ghandour, the first author of this article. the paper has been accepted and published in Nature Communications April, 2025. could you please link this preprint to the published article? <br /> Please find below the link for the published article https://rdcu.be/erKeu

Parallel processing of past and future memories through reactivation and synaptic plasticity mechanisms during sleep

Khaled Ghandour, Tatsuya Haga, Noriaki Ohkawa, Chi Chung Alan Fung, Masanori Nomoto, Mostafa R. Fayed, Hirotaka Asai, Masaaki Sato, Tomoki Fukai & Kaoru Inokuchi

Nature Communications volume 16, Article number: 3618 (2025)

looking forward to your response,<br /> Best,<br /> Khaled

On 2017-04-17 20:08:25, user JG wrote:

1) The size of the smoothing kernel (6mm FWHM) seems large given the aims of the study. Given the close proximity of the temporal pole to other brain regions, this seems to run the risk of introducing signal from still-connected tissues (e.g. IFG, insula, etc.).

2) The lesion cavity regressor doesn't make much sense because the lesion cavity signal should essentially reflect the CSF signal (which was also modeled during pre-processing) at 6 months post-resection. It also appears that the lesion resection cavity was not masked out of the analysis. This is concerning given the smoothing kernel size.

3) The majority of effects that support the authors’ conclusion are observed at a very liberal (especially for subject-level functional connectivity analyses) threshold of Z>2.33 (i.e. voxel-wise two-tailed P<0.02), and P<0.05 cluster-corrected. At the stronger threshold of Z>3.1 (i.e. voxel-wise two-tailed P<0.002) and P<0.05 cluster-corrected, the majority of effects are observed in regions that are close enough to the temporal pole that they could reasonably be artifacts of spatial smoothing.

4) It appears that the inferential statistical approach for the patients was to perform one-sample t-tests on the z-transformed correlation maps from multiple resting state runs (with 4-5 runs/participant). This seems less optimal than thresholding the maps based on the strength of the correlation computed across all runs (i.e. using temporal concatenation). Very weak but stable correlations would be significant in this analysis, and given that the correlation effect size is concealed, there is no way of telling the actual correlation strength for the reported functional connections.

This is an interesting study, but I'm not sure I fully buy the conclusions.

On 2016-06-09 00:02:24, user Adam C. Snyder wrote:

Thanks to the authors for this interesting article. One question for them is this: the argument is based on the assumption that the authors have a sufficiently accurate model of the microprocessor to stand in for the actual microprocessor of interest. By the authors' own description, one way to "understand" a system is to be able to replace any part with a synthetic version --in this case the whole system is synthesized. Well enough, in fact, to be used to support an argument. Isn't that a pretty good "understanding" of the microchip? If we had such a model of a nervous system, we would be quite pleased. Now, the model the authors use was derived using neuroscience methods (optical imaging, some rough computer vision algorithms with manual refinement, a bit of "cell bio" regarding transistors and wires, computer modelling, etc.). Does using a simulation as the "model organism" for this study undermine the argument?

On 2016-06-09 18:55:53, user Stephen Smith wrote:

Very interesting article. The point that many of the techniques we use in neuroscience measure impossible-to-interpret epiphenomena is very clearly stated. What I'm not sure I understand is, what would the authors consider a success, or what's the "goal" of brain research?

-You mentioned replacing a broken "unit" with an artificial one, but I think that would be possible for a chip. Perform "electrophsyiology" on the inputs and outputs of a transistor, and you could probably figure out the input/output relationship and solder in a new transistor. Likewise, I think there are a few other solvable problems that you could discuss:

-Can you figure out that the "point" of the system is to output a video game? For that, you would need to understand the output involves a cathode-ray beam in a TV that is lighting up pixels by sweeping across the screen. You would then need to identify the correct bit of the circuitry as the output, figure out the beam is sweeping at 60hz, figure out the coding for the RGB, ON/OFF of each pixel, number of pixels, ect. That would allow you to de-code the picture. If you started with a complete system (ie an ATARI+joystick hooked up to a TV), this might not be too much of a stretch. So, how would you look for the output-level activity of the chip? How would you identify the 'input' vs the output vs internal processing?

-Can you figure out the software? Would there be a possible way to reverse-engineer the code that's being input into into the system, based on the behavior of the transistors? Even if we do 'map' the entire brain, the software needs to be understood too...

Overall, very interesting paper. I think it could be improved if you better explore how we COULD answer these questions using the chip. This might inspire neuroscientists to think about analogous ways to answer those questions in the brain.

On 2018-10-19 09:46:37, user Marta Siedlecka wrote:

We have very similar results with perceptual awareness ratings (higher level of stimulus awareness after carrying out responses that were either congruent or incongruent with a response required by a stimulus, compared to the neutral condition)<br /> https://www.biorxiv.org/con...

On 2018-10-31 14:51:56, user Alex Naka wrote:

This is an old version of this study, being posted for posterity. Please see the revision for the most recent manuscript: https://www.biorxiv.org/con...

On 2023-03-22 00:12:42, user Jasmine Caballero wrote:

Overall, I really enjoyed this paper and appreciated the implications about the correlation between AD and COVID, since I personally had a loved one who suffered from AD who passed away after a short battle with COVID-19. Overall, I believed this is a very strong paper, but have a few comments that you may take into consideration:

In the cohort chart, I would ensure that your statistical analyses are correct. I see that in Cohort #1, you employed largely Mann-Whitney tests implying that your data was nonparametric, however in Cohort #2, you employed parametric tests (Pearson & unpaired t-test) to statistically analyze the data. Was the data set in Cohort #2 parametric?<br /> Put a reference and/or graph displaying what criteria/qualifications that are involved in ABC scoring. This would be helpful to your readers who aren’t experts in diagnosing neurological diseases and have an example of how patients are diagnosed. <br /> Figure 3 is very overwhelming, as there is so much information compact into one image. Breaking it up into 3a: Antemortem evaluation, 3b: Neurological markers, 3c: BBB markers could help readers easily visualize and digest the information and analyze trends and correlations within the heat map.

On 2016-02-05 15:16:52, user John Smith wrote:

The EWCE bioconductor package is currently under review. If you are interested in using the package then please email us and we can send a copy of the package.

On 2020-07-27 12:59:46, user Serena wrote:

Dear readers, please be aware that this work is now published after peer-review in<br /> eNeuro: 2020 May 28;7(3):ENEURO.0322-19.2020. doi: 10.1523/ENEURO.0322-19.2020. <br /> Therefore, I kindly ask you to cite the eNeuro published article. Thanks! Serena Stanga

On 2019-09-26 08:19:04, user Myles Mc Laughlin wrote:

Dear Luke and co-authors,

First of all congratulations on the nice work! I wanted to comment on a part of your discussion were you site our previous Nature Communications work (Asamoah et al, Nat Comms 2019). In the discussion you write 'Of note, our measured strength of entrainment, phase-lag values, was noticeably higher than in a preceding study in anesthetized rodents which found no direct neural effect of stimulation (27)'. This is not entirely correct. In that paper we clearly show entrainment effects that were caused by the field in the brain (see Figs. 2 and 3). We could measure significant entrainment effects caused by the brain field, even with fields less than 1 V/m as measured near the neuron (see Fig. 3, top left panel). Your statement that the strength of entrainment you measured in the monkey is noticeably higher than we measured in the rat is also not completely correct. The range of PLV values shown in your Fig. 4 look very similar to the range of PLVs shown in our Fig. 3. Note that the top left panel in our Fig. 3 is for electric field strengths which are less than 1 V/m (as measured near the neuron) and we still observed significant levels of neural entrainment. The main controversial finding from our previous work, was not that the electric field in the brain does not cause entrainment, but that the observed tACS motor system effects in humans were not caused by the field in the brain but by stimulation of peripheral nerves in the scalp. I'm interested in hearing your thoughts on this. Hopefully, there is still time to edit some of the discussion before final publication.

Best Regards,

Myles

On 2017-03-21 14:55:10, user awakenting wrote:

Did you measure the temperature of the recording site ? Couldn't that be a confound ?

On 2020-01-15 13:24:14, user Alberto Cruz-Martin wrote:

https://journals.plos.org/p...