On 2017-10-13 19:56:07, user J. M. Groh wrote:
This version contains a new supplementary figure (Supp Fig 1) supporting the correlation between how far the eyes move and the amplitude of the eardrum oscillation. Improvements have also been made to figure 2, which now shows average saccade trajectories. Changes in the text clarify logic and provide additional methodological detail.
On 2018-12-18 09:40:24, user Veit Grabe wrote:
Hello everybody,<br /> thanks a lot for sharing this nice work with us.<br /> I enjoyed reading it, but some questions arose as well.<br /> 1. Are you planning to test some higher concentrations of BA, as it seems to have a trend to reduce both behaviors?<br /> 2. I am very keen on seeing the T4/T5-GCaMP6s imaging with odors. Would you think that this should reflect the Tdc2-Chrimson activation?<br /> 3. Are you giving the LED stimulus for Chrimson pulsed? We usually have problems with inactivation of the neurons when giving a continuous light pulse.
I am really looking forward to the final publication as you are nicely showing this multisensory approach.
Cheers,<br /> Veit Grabe
On 2020-07-29 13:09:48, user Samuel Marsh wrote:
Given that both of the antibodies used to measure amyloid in IHC are human specific and the authors only used C57BL6 mice it seems likely that any results detailing staining for amyloid shown are non-specific background and not true labeling. The same appears to be true of the anti-amyloid-beta ELISA assay performed and for one of the two p-tau antibodies used. Overall this raises significant questions about the "pathology" induced by this paradigm.
To confirm if any pathology is induced by this paradigm analysis should be performed with antibodies confirmed to react to mouse amyloid-beta and specifically ones that do not also react with APP as that would further complicate interpretation of the staining. The same is true for tau staining. Mouse specific tau ELISA analysis across timepoints would also significantly boost the characterization.
On 2019-03-04 19:33:17, user Yang Liu wrote:
Very interesting! Wonder if similar phenotype will be observed in C1INH KO mice.
On 2017-04-20 19:18:45, user James Jun wrote:
Code is available at www.jrclust.org
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:
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.
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.
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.
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
On 2023-02-03 15:15:36, user Ruili Xie wrote:
The article was accepted for publication on Neuroscience:
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 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?).
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.
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.
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:
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.
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.
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.
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.
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:
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.
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.
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).
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?
Two pkc-1 mutants were stimulated by PMA but one wasn’t (Fig 6D). This discrepancy is not discussed.
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.
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.
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%.
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.
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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:
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.
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.
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.
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.
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.
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:
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.
The insets of the Fig. 2C,E,G, Fig. 3, Fig.4 and Fig. 6 should indicated in the figure legends.
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.
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?
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?
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?
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.
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:
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.
There is a typo in figure legend of figure 1, change “chromic” to “chronic”.
At page 17 line 274, “15 mM” should be “10 mM”.
At page 27 line 525, “chord”-> “cord”
At page 27 line 543, “Mouse-nc82”-> “Mouse-anti-nc82”.
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:
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.
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.
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:
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.
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.
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.
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:
On 2023-02-20 14:40:59, user Chenggang Chen wrote:
Check here for the published version:<br /> https://www.sciencedirect.c...<br /> https://www.researchgate.ne...
On 2018-11-12 14:07:51, user drvivienrolfe wrote:
Would it be useful to spell-out DTI in the title?
On 2019-10-15 09:54:26, user Loïc Fürhoff wrote:
Nice review ! It would be nice to specify that you use the 120Hz camera version of the pupil labs (if I'm right). They now sell only the 200Hz version which may produce different results. A difference from the 120Hz version is that they do not need to be focused for example, but I'm sure that they are others.
On 2020-06-05 23:43:04, user Allison Hamilos wrote:
Please note that previous versions (v1 and v2) contained a supplemental discussion. This is now formatted as a companion theory paper, "Application of a unifying reward-prediction error (RPE)-based framework to explain underlying dynamic dopaminergic activity in timing tasks," which is now available on bioarxiv at: https://doi.org/10.1101/202...
On 2022-09-13 08:13:48, user Romain Cazé wrote:
Cool name. You might be interested by my last article: https://f1000research.com/a.... Searching for collabs, always. <br /> Best,<br /> Romain
On 2020-07-01 07:31:08, user Nicolas Meunier wrote:
This work has been accepted for publication with few modifications in Behavioural Brain and Immunity.
On 2017-01-11 20:18:05, user Stephen Van Hooser wrote:
Question: what are the typical "error bars" around the points in Fig. 2f. For example, if you calculated these responses by bootstrapping across trials, would the error bars be tight or large? That is, I am asking, are the points that greatly deviate from 0 on the Y axis "real", or are those values derived from noise? How many neurons exhibit an amplitude change that is significant, by such a "within bouton" analysis? (Right now I'm looking at Fig 2F, wondering if I believe that the changes from 0 on the y axis are "real".)
On 2018-07-26 06:06:42, user yon katz wrote:
Great idea to share such data-set, and also nice study.<br /> The title is a little complex.<br /> According to the title I would expect to see patch-clamp examples in the manuscript. Thanks.
On 2019-09-23 16:43:49, user practiCalfMRI wrote:
Suggest the authors review prior discussions on apparent head motion arising out of chest motion magnetic susceptibility perturbations. Some start points:
On 2020-10-09 00:58:59, user Dieter Jaeger wrote:
Amazing work. And a very valuable article. I am wondering though what happened to the hyperdirect pathway going from layer 5 of motor and frontal cortex to subthalamic nucleus? (eg. Kita, T. and H. Kita (2012). "The subthalamic nucleus is one of multiple innervation sites for long-range corticofugal axons: A single-axon tracing study in the rat." Journal of Neuroscience 32(17): 5990-5999.) There is no mention of STN in this paper at all. Does the Mo-fl just not project there?<br /> djaeger@emory.edu
On 2018-01-22 03:21:42, user Pavel Prosselkov wrote:
No doubt you did a great job taking into an account individual player expertise as a gaming skill proficiency bias. But on the global level, gaming is more like an emergent property of our modern society with the access to it historically privileged to the high GDP countries (look at the variance distribution per country). It is that hidden but powerful dependable co-variate forcing you to conclude that Nation's GDP predicts Nation's IQ (as measured by a game).
On 2018-09-13 08:48:39, user Guillaume Sescousse wrote:
Hi,<br /> very interesting study!<br /> Just two thoughts that came to my mind when reading it:<br /> - it wasn't clear from the abstract that the effective connectivity was measured at rest. Maybe it would be informative ?<br /> - Also, I would have loved to see a figure illustrating the continuity in the distribution of dimensional phenotypes of impulsivity and compulsivity. Maybe worth adding?<br /> Just some thoughts, the paper's really nice, good luck with the peer-review process!<br /> Guillaume
On 2020-04-21 09:24:15, user John Greenwood wrote:
Now published at PNAS: https://www.pnas.org/conten...
On 2022-01-29 13:23:58, user Joseph Santin wrote:
This manuscript is currently being revised. A new version is coming soon.
On 2021-05-24 22:53:30, user ESHA CHAWLA wrote:
Recently, my biomedical research seminar had the opportunity to read and discuss your paper. Detailed below are some of the comments we had on your work.
Strengths: We appreciated your figures also diagrammed your methods – for example, Figure 1A made it extremely clear what you were knocking out and how you were doing so. We also really appreciated that your figures were not all graphs/quantitative data, but also the IHC stains that were eventually used for data analysis (i.e. Figures 1D, 2C, 3A, 6C, 7A).
Weaknesses/Suggestions: While we did appreciate the IHC images, the quantitative graphs were too small and need to be enlarged to allow for easier data analysis. Additionally, certain figures were redundant – for example, both 4A and 4B show absolute force; similarly, both 4E and 4D show stimulation frequency and CMAP amplitude. Such redundancies convoluted the conversation, as students were confused what new information (if any) they were being presented with. Finally, the images need to be made more accessible to all readers – consider using magenta/blue stains to make data readable to colorblind readers, and include arrows in IHC stains so readers know what they are looking for.
Overall, we really appreciated your scientific writing and the significance of your work – finding the mechanism by which Vangl2 interacts with MuSK to affect neuromuscular activity. We believe that with these small revisions, you can improve your data presentation and make the science more accessible to the readers.
On 2021-06-17 22:30:54, user Sasha Burckhardt wrote:
Typo? "We found a lower amount of APrE per neuron and per trial in the naïve than in the trained group (mean ± standard deviation; naïve: 0.93±0.16; trained: 0.86±0.15; effect size: Hedge’s g = 0.48)." Based on the numbers and the subsequent conclusions, APrE should be greater in naive mice, no?
On 2020-04-10 19:57:45, user Jeff wrote:
Is it complete 100% loss, or could it be significantly diminished but still present if trying really hard with a noxious stimulus?
On 2016-12-15 07:35:19, user Tmslab wrote:
Interesting work! There is actually an older paper showing similar with TMS, i.e. That imagery and visual cortical excitability are linked.
On 2017-06-28 21:10:26, user Ben Seitzman wrote:
Hello, Linden and colleagues! It appears as though this paper may have the data processing issue pointed out by Carp in 2013- http://www.sciencedirect.co...
Are you applying scrubbing before or after filtering?
On 2022-09-25 17:05:02, user Ashutosh Dhingra wrote:
Nice work. Did you see LRP1 as one of the modifiers? thank you.
On 2018-10-16 20:40:54, user Rebecca Todd wrote:
Here is an updated link to the data and scripts:
On 2023-08-22 18:06:47, user Kay Jann wrote:
This article is now published after revisions in <br /> Journal of Alzheimer's Disease<br /> https://content.iospress.co...
On 2019-06-27 20:29:09, user Pan Peter wrote:
Nice work. I am wondering whether you have apply these two inhibitors on mice. Do they also decrease OPC number in vivo? Also, I have a technical question. How do you quantify glia cell number in brain sections? Do you manually count or have a image j script that can automatically count them? Thanks.
On 2025-08-15 18:40:06, user CD07 wrote:
I think the legends are mislabeled for figure 1. The figure suggests 3/3 genotypes have more amyloid than 3/4-- the text says the reverse.
On 2017-05-26 13:14:51, user Eric Maris wrote:
Nice work! However, there is earlier work, using a slightly different methodology, but arriving at the same conclusion: http://journals.plos.org/pl...<br /> best,<br /> Eric
On 2018-07-14 12:02:20, user Brian Levine wrote:
Great paper! However I am concerned about the isolation of cued details on the AI because they are dependent on free recall. In other words, the more details you freely recall, the fewer there are at the cued stage. Thus any lack of effect of stimulation on cued recall could be due to contamination by good free recall. One way to test this would have been to conduct free and cued recall on separate memories, rather than serially on the same memory as in the standard AI administration.
On 2018-09-03 13:22:49, user scbsli wrote:
Very interesting results! Thanks so much for sharing. We have a comment regarding the Discussion on page 11 where you mentioned: "we observed in awake behaving monkeys differed markedly from previous observations in anesthetized animals (Moliadze et al., 2003; Li et al., 2017)". Actually, the neuronal activity pattern reported in your article is very similar to the one we reported in Li et al. (2017, eLife): an excitation phase between 10 and 20 ms, an inhibition (up to ca. 200 ms) and a rebound excitation that follows. Interestingly, similar results have also been reported in a study using voltage sensitive dye in the visual cortex of anesthetized cat (Kozyrev et al. 2018, PNAS) and in another awake behaving monkey study (Tischler et al. 2011, JNM). Despite differences in animal models and experimental setups, there seems to be a general response pattern triggered by a single TMS pulse. Something that's an indication of shared principles of corticocortical and/or cortico-subcortical connectivity?
Bingshuo Li and Alia Benali <br /> University of Tübingen
On 2023-07-29 14:10:46, user daniele marinazzo wrote:
How can you infer the direction of this relationship? You are saying that the correlation between BOLD time series from different cortical areas evolves (as in *the correlation evolves*), and this enhances some cortical hierarchy? At best it would be the other way around I'd say
On 2019-07-19 11:05:01, user Clockwise wrote:
I admire Elon Musk a lot... but he should include the names of intellectual contributors. One can easily see that the paper is written by group of people...even it was mentioned in acknowledgement (we...).
On 2018-09-07 12:36:40, user Benjamin Clemens wrote:
Dear Dr. Ironside,
We recently came across your manuscript, which you<br /> submitted to bioRxiv in November 2017. As part of the “International Research<br /> Training Group (IRTG) 2150 – The Neuroscience of Modulating Aggression and<br /> Impulsivity in Psychopathology” (www.irtg2150.rwth-aachen.de/) "www.irtg2150.rwth-aachen.de/)"), we offer students a comprehensive and unique qualification program. One<br /> of our students came across your manuscript while searching for appropriate material<br /> to discuss in our monthly journal club. In this part of the qualification<br /> program, students are asked to put themselves in the shoes of future reviewers and<br /> formulate constructive criticism. We really enjoyed reading your excellent<br /> manuscript and agreed that it contains important new findings that might<br /> substantially advance our understanding of emotional processing, threat<br /> responsivity and the neurocognitive mechanisms contributing to tDCS treatment<br /> effects in affective disorders. Similar to our overarching approach within the<br /> IRTG2150, your manuscript aimed to investigate causal mechanisms and new therapeutic<br /> interventions, analyzing behavioral factors and underlying changes in<br /> functional brain connectivity. Below, you will find a list of our comments and<br /> suggestions, which we hope help you to further improve your manuscript and<br /> publish it successfully.
Could you please specify your exact tDCS montage in<br /> the introduction section?
Regarding your description of amygdala functioning<br /> (page 13, second paragraph): this section provides too much detailed<br /> information on basic properties of the amygdala. If possible, shorten this<br /> section and directly relate it to your own findings. Also, we were wondering<br /> whether it makes sense to introduce these basic amygdala functions already in<br /> the introduction (but in a shortened form). For the discussion, the section<br /> should not cover basic info on the amygdala, but rather explain how previous<br /> findings about the amygdala relate to your own work.
Regarding your description of tDCS mechanisms (page<br /> 3, last paragraph): while you mention NMDA receptor dependency, other, more<br /> basic and important issues are omitted (e.g. anodal vs. cathodal effects on<br /> excitability, membrane potential effects). We feel that this paragraph would<br /> benefit from expanding, making it more detailed and comprehensive. This will be<br /> especially helpful for all readers not familiar with tDCS and underlying<br /> mechanisms.
Please clarify in the introduction, why exactly you<br /> chose to measure females only. Also, including references supporting your claim<br /> of higher prevalence of trait anxiety in females is strongly recommended.
While discussing the methods section, we all agreed<br /> that your description of the conducted analyses does not provide enough detail<br /> to fully understand / reproduce it. For example: how did you choose the alpha<br /> level for power analyses? Did you use the same values for whole-brain and ROI<br /> analyses? Which values were used for these analyses, beta values or others? Did<br /> you actually concatenate your 3 runs in the ‘intermediate’ analysis (page 8)?<br /> Why is your interval between blocks just 2 seconds while your intertrial interval<br /> is 3 seconds?
The sentence about excluding and including<br /> additional participants to get to N=16 should be rephrased (page 5, first<br /> section). In its current form, it causes confusion. Also, please provide info<br /> on whether you excluded participants in case of psychiatric diagnosis other<br /> than depression. Since you used the SCID, it would be interesting to know<br /> whether your participants actually met the criteria for anxiety or personality<br /> disorders. This would be extremely important for interpretation of your<br /> results, but there is no info on this point in the manuscript. Also, it is<br /> standard practice to assess data on intellectual capacities, years of education<br /> and other neuropsychological parameters. If you have these data, please include<br /> them. In your case, especially data on attentional processing (alertness,<br /> vigilance) would be interesting, as attentional functioning represents an<br /> essential component of your experimental task.
Unfortunately, there is no visual depiction of tDCS<br /> current flow. Please simulate current flow resulting from your chosen montage<br /> and include pictures, to better visualize brain regions affected by tDCS<br /> current. This might also help you to specify the discussion a bit more.
Were there any supra-threshold clusters for<br /> sham>active for the whole brain analyses? This is especially interesting<br /> since you apparently found no anodal stimulation effect in the right DLPFC.<br /> This should be discussed / explained in your manuscript at some point.<br /> Specifically when using the anodal-right cathodal-left montage, it seems that<br /> some rather complex effects in DLPFC regions are to be expected. Why do you<br /> think you did not find any change in the right DLPFC?
To really conclude that there is a specific tDCS<br /> effect in people with high trait anxiety, there has to be some sort of control<br /> group (anxiety disorder patients, healthy subjects scoring low on anxiety<br /> questionnaires). If you cannot include such a group in your current study, please<br /> at least elaborate on potential differences between your sample and such a<br /> control group in your discussion.
To fully understand your experimental procedures<br /> and the fMRI task, please include an additional figure with fMRI timeline for<br /> an individual trial.
Changing the scale in Figure 3b is inappropriate<br /> and misleading. Please try using identical scales for all parts within one<br /> figure. Also, we were wondering why and how you combined data for FEF/SFS and<br /> SMG/STG? Better provide data for individual brain regions separately. For<br /> example, FEF and SFS refer to anatomically and functionally different regions<br /> of the brain.
Table captions should also include info about<br /> which coordinate system your data are presented in.
Although you mention previous studies showing that<br /> threat reactivity effects occur only in low-attentional load conditions, you<br /> should try to explain the reason for this in the discussion. Why does tDCS not<br /> change threat reactivity in the high-load conditions? Why are the effects<br /> confined to the low-load condition?
Either in the discussion or in the introduction,<br /> you should mention why you choose the bilateral montage. Also, please discuss<br /> the absence of fMRI effects underneath the anodal electrode. Why do you think<br /> is there no anodal stimulation effect in the targeted area?
Why don’t you provide an explanation for the<br /> behavioral changes in task accuracy?
Please explicitly mention limitations of the<br /> current study (e.g. no control group). While you sort of hint at these<br /> limitations a bit in the discussion, they should be explicitly mentioned as<br /> limitations to avoid confusion for the reader.
Maybe you can combine your limitation section with<br /> a few suggestions for future work, such as including eye-tracking data to<br /> verify the attentional focus of participants, to know where they were actually<br /> looking during the task.
Overall, we think that your manuscript is<br /> well-written, your experiment is well-designed and your study provides<br /> important and novel results. We hope our comments help you to improve the<br /> current version of your manuscript. In case you have any questions, please feel<br /> free to contact us (www.irtg2150.rwth-aachen.de/) "www.irtg2150.rwth-aachen.de/)").
Best regards from Aachen, Germany
Leandra Nolte, Carmen Weidler, Lena Hofhansel, Jana<br /> Zweerings, Halim Baqapuri, Hannah Kiesow-Berger, Laura Bell, Teresa Karrer,<br /> Jeremy Lefort-Besnard, Rebecca Paetow, Alexandru Puiu, Ramya Rama, Simon<br /> Koppers, Susanne Stickel, Philippa Hüpen, & Benjamin Clemens
On 2017-07-11 18:46:57, user Zoe Donaldson wrote:
Very interesting. Wondering how you interpret the results from Amadai et al. (https://www.nature.com/natu... "https://www.nature.com/nature/journal/v546/n7657/full/nature22381.html)") where authors argue that activation of PL-NAcc neurons selectively in a social chamber contributes to formation of a social preference (although worth noting that this preference may not be sig different from null hypothesis, only from controls). Their stim frequency is very different - 5Hz and obviously a different species but do you think you would see the same thing if you drove activity only when the mouse was investigating a social partner under a cup and not the rest of the time. Would this fit with your synaptic weighting hypothesis?
On 2025-10-11 17:34:27, user Vikram Shakkottai wrote:
This manuscript has now been published at <br /> https://onlinelibrary.wiley.com/doi/full/10.1002/ana.78042
On 2020-05-27 19:55:25, user anonymous viewer wrote:
The manuscript mentions "We and others have recently reported results of scRNA-seq studies on human retina (55-60)... Three of these groups, however, used adult retina and separated fovea from peripheral retina. Although these studies generated valuable data, they were disadvantaged in that rods comprise a large fraction of all cells (>70%), reducing power to distinguish cell types among less abundant classes."
This last sentence is not correct. There are three available studies with foveal (or macular) versus peripheral analysis: <br /> Liang et al: 44.8% and 53.3% of cells were rods.<br /> Voight et al: 19.4% of cells were rods.<br /> Sridhar et al: work includes several independent experiments on fetal retina, none of which contain a majority of rod photoreceptors.
On 2023-06-11 09:47:50, user Vinod Kumar Gupta wrote:
From 3 decades the adaptive nature of cortical spreading depression (CSD) --both neuronal and neuro-vascular--has been elucidated in lower animals.
*The damaging consequences of CSD are purely serendipitous and/or imaginary.***
Migraine serves as the best template to eliminate any pathophysiologic role for CSD. While migraine is a lateralizing painful disorder, CSD has never been linked with pain or nociception.****
i>Scintillating scotoma of migraine -- the pathognomonic feature -- has never been described or drawn as a homonymous defect, ruling out cortical origin and/or role of CSD.
REFERENCES
*Gupta* VK. Pathophysiology of migraine: an increasingly complex narrative to 2020. Fut Neurol. Published 24 May 2019. https://doi.org/10.2217/fnl....
Gupta VK. Cortical-spreading depression: at the razor's edge of scientific logic. J Headache Pain. 2011 Feb;12(1):45-6. doi: 10.1007/s10194-010-0287-z
Gupta VK. Cortical spreading depression is neuroprotective: the challenge of basic sciences. Headache. 2005 Feb;45(2):177-8
Gupta VK. CSD, BBB and MMP-9 elevations: animal experiments versus clinical phenomena in migraine. Expert Rev Neurother. 2009 Nov;9(11):1595-614. doi: 10.1586/ern.09.103.
Gupta VK. Migrainous scintillating scotoma and headache is ocular in origin: A new hypothesis. Med Hypotheses. 2006;66(3):454-60. doi: 10.1016/j.mehy.2005.11.0
ORCID: 0000-0002-6770-5916<br /> 11 June 2023<br /> New Delhi, India
On 2019-06-26 22:21:24, user Stef Garasto wrote:
Link to the peer-reviewed paper: https://ieeexplore.ieee.org...
On 2016-11-21 08:47:36, user Alexandre ABRAHAM wrote:
A newer version of the article is available here: https://hal.inria.fr/hal-01...
On 2023-10-12 18:45:10, user John O'Donnell wrote:
This is a highly sophisticated and translationally relevant study, but its translational relevance is negatively impacted by referring to it as severe TBI. There does not appear to be any coma, let alone loss of consciousness lasting more than 24 hours, that would justify classifying the injury as "severe". Rodent studies often hijack the mild/severe clinical terminology to give the appearance of translational relevance even though the models do not fit the requirements for classification into any of the clinical TBI severity criteria. This important work will be easier for clinicians to interpret if the language is made more accurate and avoids redefining established clinical terms.
On 2020-07-02 11:08:30, user Emily Hird wrote:
Please note that in Dataset 1, an error in the experimental script meant that each subject received one extra trial from the condition 'Cued intensity 2 Stimulus intensity 4' and one fewer trial from another random condition. Dataset 2 was not affected by this issue.
On 2023-10-15 07:41:28, 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 2019-04-13 10:44:41, user GPT wrote:
this is the best approach. information about the diverse subtype are useful not only for disease progression ptrediction but also for future therapy. I think that this point should be stressed. the best result for the future therapies should be tailored the treatment to the different PK subtypes.
On 2017-08-30 00:53:42, user Seanny123 wrote:
Is this related to Conceptors at all?
On 2016-03-26 13:08:36, user Peter Murray-Rust wrote:
We kicked off a similar thing for chemistry in blueobelisk.org . It's been very successful even though there has been no funding. The key things are mailing list, shared site/wiki and a desire to share rather than control or conquer. See https://en.wikipedia.org/wi... which includes papers. The intensity waxes and wanes as everyone has other calls on their time. I give out small blue obelisks as prizes and I think this is a small additonal motivation!
On 2024-05-07 13:42:17, user Iman Aganj wrote:
This article has now been published in Frontiers in Neuroscience: https://doi.org/10.3389/fni...<br /> The LC labels are publicly available: https://www.nitrc.org/proje...
On 2019-03-18 08:22:54, user Ujváry István wrote:
I did not know that 5-MeO-DMT was 'endogenous', that is biosynthetically produced in the human body.
On 2021-01-29 23:59:01, user paige_leary wrote:
Main Findings:
The manuscript describes a series of experiments to investigate the computational framework and neural substrate that might underlie a modifiable sensorimotor behavior, the optomotor response. Data from closed-loop behavioral experiments can be described with a control-theoretic model designed to map on to brain areas. Neural activity associated with optomotor behavior was measured across the entire larval zebrafish brain with light sheet microscopy. One possible substrate that could encode task-relevant parameters, the Purkinje cells of the cerebellum, was characterized further with chemogenetic loss-of approaches and more refined imaging.
This manuscript addresses an important problem in behavior: how does a reflex behavior effectively cancel the stimulus that evoked it? When the stimulus changes, how does the reflex change to compensate? In presenting this paper for our lab’s babka-themed journal club, I learned about the utility and limits of feedback controllers and internal models in stimulus-driven actions. The manuscript studies these concepts in the context of an accessible reflexive behavior and proposes a means by which neural circuits can adaptively adjust sensorimotor reflexes.
Major Concerns:<br /> 1. We suspect traces have been mislabeled in Figure 2e. For example, the condition with a reafference gain of 0 (Figure 2ei, yellow trace) should be identical to those with infinite reafference lags (Figure 2eii-iii, yellow traces), but the trace data do not match. This is inconsistent with the Methods Section stating that these reafference conditions are redundant (line 950).<br /> 2. In Figure 4g, is unclear why the ballistic period bout power of the pre-adaptation trials, where reafference is normal, is distinctly different from the ballistic period bout power of the normal reafference trials in Figure 2e. This is further complicated by the manuscript stating that the ballistic period is not changed depending on the reafference condition (line 300), despite these two conditions having the same (normal) reafference. If bout power was computed in a different way, it should be clearly stated.<br /> 3. The behavior adaptation paradigm presented in Figure 4 is performed again in Figure 5 to demonstrate the crucial role of Purkinje cells in long-term adaptation. While it is expected that the bout duration traces of the lag-trained non-ablated fish (Figure 5bi) and the lag-trained fish (Figure 4c) should match, they do not. The lag-trained bout durations in Figure 5bi present with a smaller peak bout duration during the first 10 adaptation trials (below 0.5s), and a sharp decay to near-baseline. In contrast, the lag-trained bout durations in Figure 4c present with a greater peak bout duration during the first 10 adaptation trials (near 0.6s), and a gradual decay to near-baseline. We propose that this may reflect behavioral differences due to differing genetic backgrounds. However, it is unclear if these experiments were run on larvae of the same age, as the Methods Sections states that the experiments in Figure 5 were performed on 7dpf larvae (line 850) and the experiments in Figure 4 were performed between 6-8dpf (line 861). We suggest addressing this discrepancy and discussing possible sources.<br /> 4. In Figure 6f, the manuscript states that the ROI activity profile unique to lag-trained adapting fish (barcode “0-0+”) is the expected activity profile for an internal model, as it would gradually recalibrate to a long-lasting motor-to-sensory transformation change (lines 406-408). However, the behavior paradigm outlined in Figure 6b presents two distinct adaptation phases: the primary adaptation phase (reafference is lagged) and the post-adaptation phase (reafference is reverted to normal). It is unclear why the ROI activity profile characteristic of an internal model would only present activity during the second adaptation phase, and not the first. We recommend further explanation of this conclusion.<br /> 5. Figure 7a presents the proposed cerebellar internal model as a forward internal model. The manuscript justifies this claim of a forward model by citing studies implicating the cerebellum as a site of forward models (lines 504-508), and by citing studies implicating inferior olive activity as a teaching signal for Purkinje cells, while referencing the sensory ROIs found in the inferior olive in Figure 3f (lines 527-532). Due to the considerable experimental difficulty of distinguishing a forward and an inverse internal model, we suggest explicitly stating in the introduction how the manuscript would expect to discriminate one from the other.<br /> 6. (Lines 380-385) Presumably this analysis could be repeated to look for comparable ROIs in the whole-brain dataset that includes the cerebellum. Currently the manuscript (line 95) assays “whether neuronal requirements of [recalibration] are met in the larval zebrafish brain” but only finds that to be true for a minority of fish assayed; other fish don’t adapt or their cerebelli don’t show comparable patterns. The manuscript would therefore benefit from applying a similar analysis to the whole-brain dataset to increase confidence in this fundamental finding.
Minor Comments:<br /> 1. Line 81: should be “normal” not “normally”.<br /> 2. Line 438 (and elsewhere): using the word “tiredness” to describe a model parameter that limits bout duration is needlessly confusing. It links an internal state to sub-second bout kinematics.<br /> 3. Figure 5a: should be “wild” not “wyld”, and “control” not “conrtol”
--Babka Club, 01/29/2021. Paige Leary, Franziska Auer, Kristen D'Elia, Dena Goldblatt, Kyla Hamling, Yunlu Zhu, David Schoppik.
On 2017-11-19 13:34:09, user Seanny123 wrote:
What's the difference between this model and the motor model of Travis DeWolf? http://compneuro.uwaterloo....
Edit: Nevermind, I see now that it isn't modelling motor control at all. Speed-reading strikes again.
On 2019-03-18 17:24:43, user Konrad Lehmann wrote:
I'm not on Twitter and never will be, but I feel the urge to reply to Santiago Rompani's tweet, so I do it here: We have of course been aware of this alleged confounder. In consequence, we have addressed it three times throughout the manuscript: in methods, in results, and in a long first "methodological considerations" section in the discussion. We can moreover refer to our previous paper, where we have already conclusively shown that running during four days of MD has NO effect on OD plasticity. It doesn't. Full stop.
On 2020-05-13 01:39:29, user Craig Meek wrote:
I have seen a video with Elon musk talking about this on the Joe Rogan podcast , this is a serious futuristic leap for mankind! Which made me think if this device can potentially fix anywhere in the brain. Is it possible to fix people who suffer from cerebellum hypoplasia?
On 2018-12-19 00:10:24, user BU_FALL_BI598_G3 wrote:
Shank3 is a multidomain scaffold protein located at postsynaptic sites known to modulate baseline synaptic transmission as well as intrinsic excitability in some brain areas. Mutations in Shank3 are associated with several disorders including ASD and Phelan-McDermid syndrome. In this study, Tatavarty et al. sought to explore the role of Shank3 in homeostatic plasticity. In knockdown (KD) cells where Shank3 expression was reduced by 50% (analogous to haploinsufficiency in human Shankopathies), synaptic scaling and intrinsic homeostatic plasticity were completely absent, indicating that Shank3 is required for these homeostatic plasticity mechanisms. Treatment with lithium (Li), a substance used to treat several neurological disorders, restored both synaptic scaling and intrinsic homeostatic plasticity in Shank3 KD neurons. Inhibition of GSK3, a target of Li, also rescued synaptic scaling in Shank3 KD neurons. Finally, in Shank3 knockout (KO) mice, homeostatic restoration of firing rate following monocular deprivation was absent in neurons of the visual cortex.
Overall, we feel that this paper is a concise investigation into the basic neuronal dysfunction due to loss of Shank3; such basic exploration is a necessary addition to the study of neural disorders and should be done in vitro, as done by the authors here. The additional use of in vivo models strengthens the results found in culture and increases the relevance of the paper. This study also provides useful information regarding possible mechanisms of action of Li, supporting its use as a treatment for neurological disorders such as ASD.
Despite the utility of the results found in this paper, we do have some concerns as well: <br /> 1. The authors first use RNA interference to create a KD of Shank3 in cultured neurons to model haploinsufficiency in human Shankopathies. However, when the authors move into in vivo experiments, they use a Shank3 total KO mouse. It is unclear how firing rate homeostasis in a KO model corresponds to synaptic scaling and intrinsic plasticity in a KD model. We would like the authors to discuss how these two models compare, as well as how analogous a Shank3-/- mouse model is to the genetic basis of human Shankopathies. Some experimentation in cultured Shank3 KO neurons could help with these necessary comparisons. <br /> 2. There are several controls and comparisons missing from Figures 1 and 2. Figure 1 should include images of GluA2 and Shank3 in dendrites, as well as example raw traces and average mEPSC waveforms, after PTX treatment. We would also like the authors to include a rescue experiment with RNAi-resistant Shank3 after PTX treatment (as in Fig. 1F) to further support their claim that Shank3 regulates bidirectional synaptic scaling. Additionally, we would like Figure 2 to include many more control cases. In particular, Figure 2B should include untreated control, control + TTX, and control + TTX + Li conditions. The cumulative histograms shown in Figure 2C should be compared to more control conditions as well, especially control + TTX treatment. These controls are vital to accurately determining the effects of Shank3 KD and Li treatment on synaptic scaling.<br /> 3. While regulation of firing rate is an important aspect of homeostatic regulation (and the authors do address firing rate homeostasis adequately), it has been previously shown that intrinsic plasticity may also regulate firing pattern. To address this possibility, we ask the authors to investigate firing pattern homeostasis in their Shank3 KO in vivo data.
We additionally have a few minor critiques. When referencing Gideons et al. (2017), the prolonged treatment with Li was for about two weeks, which is not comparable to the 24 hours of Li treatment used to study basal postsynaptic and neuronal properties in this paper. In the monocular deprivation experiment, we would like the authors to include the length of the recording sessions from which firing rates were determined. In general, the labelling system used in the figure legends should be clearer and more standardized to assist readers’ understanding of the experiments. For example, in Figure 2 it is unclear what UT stands for; this condition should be more clearly labelled. In Figure 2D, the figure legend states that these cumulative histograms are from neurons treated with TTX + DMSO (vehicle) or TTX + GSK3i for 6 hours. It is unclear if in all other cases without Li treatment DMSO was also applied as a control. Finally, future experiments should observe the monocularly deprived mice for longer time periods to investigate if Shank3 KO simply makes the recovery period slower, or actually abolishes the homeostatic response of the neocortical neurons. It would also be interesting to explore whether Li treatment restores homeostatic response in Shank3-/- mice during monocular deprivation.
On 2018-12-16 23:05:03, user BU_Fall_NE598_Group2 wrote:
Summary: <br /> DeNardo et al. were able to identify changes in cortical activity regarding the standard model of memory consolidation over 28 days. The authors use a new TRAP2 mouse line to address their question regarding the role of the prelimbic cortex (PL) for memory consolidation and retrieval. By identifying and quantifying TRAPed cells at specific timepoints, DeNardo and colleagues were able to elucidate the dynamic changes in cortical memory trace ensembles over time. Specifically, by analyzing contributions of TRAPed cells during 1, 7, or 14 day memory retrieval, researchers found that ensembles TRAPed at later time points had higher contributions to 28 day remote memory retrieval. These findings support the standard model of memory consolidation, that increased cortical ensemble recruitment occurs in the first two weeks after learning when a memory is “transferred” from the hippocampus to the cortex.
DeNardo and colleagues were able to complete these experiments with a new TRAP2 mouse line. Researchers explained how they generated a new strategy for ‘targeted recombination in active populations’ (TRAP), termed TRAP2. The TRAP method harnesses an immediate early gene locus to drive expression of a tamoxifen-inducible CreER; neuron activation in the presence of tamoxifen allows genetic recombination to permanently express an effector gene. The previous version of TRAP was found to disrupt endogenous Fos, a marker of neural activity, and also did not adequately infect several brain regions. This inhibited researchers from being able to relate the activity of cortical neurons during learning/recent memory retrieval to their function in remote memory.
In Figure 1, the authors provide schematics for the TRAP2 design and provide various measures used to characterize the transgenic mouse line. Here, the researchers show that the TRAP2 mouse line is effective in a variety of brain regions based on quantification of Fos expression. Figure 2 provides characterization of PL activation patterns during fear conditioning and memory retrieval at the aforementioned timepoints and demonstrates how cells TRAPed at later time points have higher contributions to remote memory retrieval.
To establish a causal role for TRAPed PL neurons in remote memory retrieval, researchers employed chemogenetic and optogenetic manipulations, as displayed in Figure 3. Chemogenetic inhibition of the PL during fear conditioning, followed by later optogenetic activation of 14 day recall TRAPed ensembles, did not produce light-induced freezing, as in animals who were not inhibited during fear conditioning. Figure 4 includes data from a whole-brain analysis. Following photostimulation of TRAPed neurons in a mouse’s homecage, researchers evaluated Fos expression in a variety of brain regions to gain a better understanding of the brain-wide memory network from a cellular perspective.
Merits:<br /> Overall, this paper is effective in communicating how prelimbic cortical neuron ensembles change with time. The authors provide convincing evidence through a variety of techniques that support their claims and conclusions.
Additionally, this paper offers an improved tool, TRAP2, with corresponding characterization in a transgenic mouse line. DeNardo et. al. make a convincing argument for the adoption of TRAP2 over the original TRAP1.
TRAP2 can be effectively used alongside immunological tools such as iDISCO+, as well as computational methods such as t-distributed stochastic neighbor embedding (t-SNE) to investigate memory circuits on a brain-wide scale.
Specific Critiques:<br /> Figure 1 explains the design and characterization of TRAP2 by comparing cell fluorescence in the presence or absence of fear conditioning. Although the authors include a detailed comparison of TRAP1 vs. TRAP2 in Extended Data Figures 2 and 3, including such a comparison in a main text figure will more strongly emphasize the need for this new tool. Additionally, there is no figure depicting the endogenous expression of Fos of TRAP1. Including this would provide more validation of DeNardo et al’s claim that TRAP2, unlike TRAP1, does not disrupt the endogenous Fos pathway.
When characterizing the effectiveness of TRAP2 labelling vs. TRAP1 labelling in Extended Data Figure 3b, the authors do not explain the considerable non-zero tdTomato labelling in the absence of 4OHT. The authors also do not show if TRAP1 has the same basal tdTomato expression in the absence of 4OHT or if this leakiness is a result of the new mechanism of expression. Furthermore, since this basal expression is nonzero, all results should be normalized to appropriate controls in the absence of 4OHT.
The schematic included in Figure 2B of one brain hemisphere with what appears to be the merged image from Figure 2C laid over the PL should be identified and explained in the legend. We assume that the bigger red dots are merged TRAP/Fos puncta and the green dots represent Fos expression, but it is unclear. Alternatively, the figure could be expanded in Figure 2C or removed entirely.
The authors indicate in their Methods section that a software called FreezeFrame was used to quantify mouse freezing behavior, except during optogenetic experiments where wires occluded the view and manual observation was used. In order to verify that manual methods closely matched those of automated freeze detection, matching controls should be performed on mice (without optogenetic cables) using both methods simultaneously--ideally, both methods should capture the exact same time points of freezing. Furthermore, specific criteria used during manual observation of freezing should be listed in the methods.
This manuscript heavily relies on one kind of behavioral output (freezing) to measure the effects of TRAPed cells. Testing the function of TRAPed cells with other behavioral assays to measure the occurrence of anxiety or fear following photostimulation would strengthen the manuscript. Consider elevated plus maze or open field tests to determine how TRAPed PL cells influence behavior.
Minor Concerns: <br /> In general, none of the schematics or figure legends are very clear or informative about the experiments that were performed. Specific explanations in text and within the figure legends are needed so that readers less familiar with the TRAP technology and memory consolidation experiments can also glean the importance of these results.
The authors could also look into improving the quality of the images used for histology. Increasing the magnification of these images and retaining the original fluorescent colors (Figure 2C) would permit better comparison across the TRAP, Fos, and merge images.
There was also some discrepancy between optimal cFos expression. Brains were collected an hour after behavior to determine cFos expression. However, previous research has shown that a 90 minute window is most effective for maximal Fos expression.
For experiments presented in Figure 3, the authors inject an AAV expression hM4Di (bilaterally) in addition to Cre-conditional ChR2-eYFP (unilaterally) into the same region (PL). Authors should expand on their reasoning for only injecting the ChR2 unilaterally. Additionally, authors should take into consideration the possibility of confounds for viral expression/effectiveness, as well as cytotoxicity, when injecting a cocktail of viruses into the same brain region. Furthermore, with so many different mechanisms at play simultaneously (e.g. transgenic mice, TRAP mechanism, ChR2 optogenetic activation, DREADD hM4D expression, CNO injection), one has to wonder if the amount of potential variables in the system could vastly confound the results.
In the last paragraph of pg 2, the authors claim that “reactivating TRAPed cells during presentation of the CS+ and CS- was not sufficient to increase freezing above the level of the tones” and make reference to Figure 3D, as well as Extended Data Figure 7a and b. First of all, it is unclear what “above the level of the tones” means, as all three plots show results in response to tones so there is no comparison to a “non-tone” condition that could produce such a statement. Secondly, although in Extended Data Figure 7a and b, ChR2 stimulation does not increase the amount of freezing in the CS- or CS+ altered context states, in Figure 3D, ChR2 stimulation clearly has a significant impact on freezing under the CS+ normal condition, as p-values are all below 0.0001. Thus, the authors need to revise their conclusion made here or better explain the claim they are making.
In Figure 4h, the authors claim that principal component analysis can distinguish two distinct populations along PC2; however, the separation chosen seems rather arbitrary and the two groups identified do not seem to represent distinct populations. A more refined analysis may be needed to truly claim that these groups are significantly different.
Future Directions: <br /> Calcium imaging could be used to record dynamic cell activity during behavior. This would provide more information on the temporal dynamics of activation, as well as how the ensemble is modified overtime. Researchers could more closely track the remapping of ensembles and determine if reflected behavioral changes in real time. Tracking such modifications over time could give researchers a better idea of how to approach this circuit from a molecular perspective.
Although it is not the focus of the paper, further characterization of the TRAP2 mechanism may be needed in order to understand the particular ensembles that are being targeted by TRAP. Is there a minimal activity level that needs to be sustained in order for cells to be TRAPed (i.e. is the TRAP2 method biased towards hyperactive neurons)? Perhaps cells that do not meet a threshold activation level may be important for memory consolidation but may contribute to the network in different ways (i.e. sporadic, low frequency activity, inhibited activity, etc.) than tonically active cells or cells that increase their firing rates.
As briefly mentioned in the abstract and discussion, the hippocampus is crucial for memory formation and consolidation. In future studies, it would be compelling to further investigate the dynamic changes of hippocampal ensembles during consolidation and remote memory retrieval. Repeating these experiments targeting the dorsal dentate gyrus, known for encoding context-specific memories, would allow for a deeper understanding of the hippocampus’ role in this neural circuit.
Additionally, quantifications of TRAPed cells in the hippocampus at the same time points used in PL experiments could give rise to evidence supporting the standard consolidation theory between the hippocampus and cortex. Ideally, the hippocampus would show an initially high number of TRAPed cells, decreasing over the 28 day time-course, while the PL would show an initially low level of TRAPed cells, with increased ensemble size at later time points.
The infralimbic cortex (IL) has interactions with the prelimbic cortex (PL). It would be interesting for the researchers to analyze the influence of IL activation on the PL, such as whether the inhibition of IL would influence the PL and significantly influence fear conditioning behavior in mice. A protein specifically expressed in IL could be inhibited in function, and its effects could be analyzed to determine whether this projection pathway also plays a role in fear conditioning and memory in mice.
Future studies could investigate if these findings observed here for fear memory consolidation and retrieval holds true for memories of a positive valence. Repeating the experiments by tagging a positive memory such as female exposure would provide evidence to better characterize dynamic cortical changes for consolidation and retrieval.
On 2021-01-13 21:24:56, user Vivek Kumar wrote:
Published Peer Reviewed Version is now avaialble online@
https://doi.org/10.1007/s00...
Kumar, V., Krolewski, D.M., Hebda-Bauer, E.K. et.al.,<br /> Optimization and evaluation of fluorescence in situ hybridization chain reaction in cleared fresh-frozen brain tissues.(2021)
On 2017-05-11 14:38:42, user Remi Gau wrote:
Minor detail: axes of the figures might need some labels.
On 2017-10-20 15:32:01, user meh wrote:
Ummm? Subcortical SNR in HCP data has been shown to be EXCEPTIONALLY poor (http://mvpa.blogspot.com/20... "http://mvpa.blogspot.com/2017/09/voxelwise-standard-deviation-at.html)") <br /> so any 2nd order analyses done ( especially where your correlations are zero! :-/ ) are a dubious prospect at best!
On 2018-04-23 14:01:45, user MikeXCohen wrote:
Really nice work and beautiful data. I have a question about the conclusion of orthogonal data features, and I hope I can trouble you to consider the following observation and suggestion.
In non-invasive imaging, it is well-established that although PCA(/SVD) is great for compressing the data from M (channels/neurons/voxels) dimensions to C (where C is smaller than M but greater than 1) dimensions, PCA is not a good method for separating sources (that is, interpreting individual C's), mainly because of the orthogonality constraint. Even in simulated data with high SNR, PCA typically cannot accurately reconstruct simulated spatiotemporal dynamics. This fact -- and the distinction between dimensions and sources -- seems particularly important when one is making a claim about orthogonal sources of variance in multidimensional data. The PC weights are guaranteed to be orthogonal, and thus the PC time series are going to be largely orthogonal as well (non-orthogonality in component time series will result from non-stationarities in the covariance structure over time, because PCA assumes covariance stationarity).
So it seems to me that any PC-based decomposition will naturally lead to the conclusion of orthogonality, even in non-orthogonal data. One can think here of the typical PCA-vs-ICA distinction with the "X"-shaped data (e.g., https://i.stack.imgur.com/O... "https://i.stack.imgur.com/OB1Mz.png)"). In that toy example, the claim that the two PCs are orthogonal is valid (trivially so), but that does not accurately reflect the structure in the data, which can be revealed only by a non-orthogonal decomposition. In R^10000 it is impossible to visualize the data, but it still seems to me that imposing orthogonality might have the consequence of missing the most enlightening insights in the data.
I think a possible solution here (IMHO) might be to use SVD to compress the data to 100 dimensions, and then perform an ICA, generalized eigendecomposition, or factor analysis (PCA + non-orthogonal rotation) on those orthogonal components. One could then inspect the IC basis vectors for task-relevance and orthogonality, and only then would an inference of orthogonality be a non-trivial result of the structure in the data, as opposed to a bias resulting from the analysis constraints. The non-orthogonal basis vectors are more likely to correspond to sources (networks), which is arguably of greater interest than dimensions. The question of whether to use ICA or generalized eigendecomposition is an important one, and depends on whether one assumes the key sources of variance are Gaussian (GED) or nonGaussian (ICA).
Thanks,<br /> Mike Cohen
On 2020-04-07 09:14:57, user Jun Kitazono wrote:
You can get supplementary material and high-resolution figures from the link below. Please see “papers” folder in this toolbox.<br /> https://github.com/oizumi-l...
For some reason, supplementary material is missing and the image resolution is low in “v1”. Maybe something was wrong with a direct transfer process from a journal to bioRxiv. I’ve re-uploaded directly to bioRxiv supplementary material and high-resolution images. Now they are undergoing a basic screening process. I think they will be uploaded in a few days. I’m sorry for your inconvenience.
On 2025-07-12 17:06:56, user Rebecca Pompano wrote:
Hello, I found the topic of this preprint very interesting, as it can be difficult to be certain which lymph nodes drain the brain after injection, and for how long. It would also interesting to estimate from these data what fraction of injected tracer remains in the lymph node versus in the rest of the animal over time, for the various modes of delivery.
As someone who is not used to looking at these types of biodistribution images, I wonder whether it would be possible to add schematics that illustrate the orientation of the animal in the various figures? And/or label the key features in the images (e.g. to point out the injection site, bladder, the various lymph nodes, etc). This would help a lot to make it clear which lymph nodes were labeled at each time point.
Thank you for sharing your work!<br /> Rebecca Pompano<br /> University of Virginia
On 2020-08-16 20:51:46, user Kaylee wrote:
In Figure 4E-F, it seems like the overlap for P not M.
On 2025-01-13 10:10:26, user Karin Vadovicova wrote:
Dear Authors,
I enjoyed your excellent experimental prove that mu-opiods activate medial habenula. I am curious about your opinion. I wonder if optogenetic activation of MHb can test my idea that MOR and some anesthetics activate the MHb->IPN-> MRN-> serotonin->claustrum ->cortical SWA circuit ?<br /> These agents can through slow-wave activity (SWA) in in cortical and striatal neurons induces loss of awareness.<br /> I also predicted that MOR activation in MHb causes opioid-induced respiratory depression OIRD. Thus targeted MOR activation speficaly in MHb should cause respiratory depression OIRD, with high morphine dose even respiratory arrest. I am very interested for your feedback. Best regards,<br /> Karin Vadovicová
DOI: 10.13140/RG.2.2.29677.97766<br /> Circuits for anesthesia, unawareness, OIRD, sleep and memory replay: MHb->IPN->PAG + DRN + MRN->claustrum->cortex.<br /> December 2023
On 2017-03-16 08:48:44, user Róbert Bódizs wrote:
Dear Colleagues! <br /> The issue of individual differences in slow and fast sleep spindle frequencies, as well as the problem of individual- and derivation-specific amplitude criteria are the main focus of our research group since 2004. We developed a new conceptual framework and methodology in order to address these issues. These works were published in several scientific journals including Journal of Sleep Research, Journal of Neuroscience Methods, Frontier in Human Neuroscience, Journal of Neuroscience, Scientific Reports, Developmental Psychology, etc. It is strange that these paper are not cited at all in this interesting report. This is embarrassing not only because these papers will not be considered as forerunners of this work. Three of the authors of the present report were sitting in front of me in 2016, and were listening to my 50 minute talk on the issue (The 1st International Conference on Sleep Spindling, Budapest). In this talk and in our papers published with my colleagues I explicitly reviewed the variability of the frequency criteria of spindles in different publications and the problems raised by this variability. This is seen now in the introductory part of the present report. I also mentioned (and published) that the 9-16 Hz range is the primary range of sleep spindles in humans. I even deduced it from previous papers. This range is now used in the present report, without mentioning our respective papers. Last, but not least, our efforts in converging the frequency criteria of the individual adjustment method and fixed frequency methods of sleep spindle analysis resulted in a paper concluding that the 12 or 12.5 Hz border is more appropriate for delineating slow and fast sleep spindles in adult subjects than the frequently used 13 Hz limit. This is again used in this report without citing the original paper published in Frontiers in Human Neuroscience in 2015 (based on the data of ~160 healthy adult subjects). It would be great to see that these shortcomings will be completed in the revised version of the current report. <br /> Sincerely yours, <br /> Róbert Bódizs