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Hi FSL users,
any hint on how to use FSL topup on two phase encoded DTI scan each with 63 directions and 1 b0, the data is actually from PPMI dataset (PD patients) and am trying to correct for susceptibility distortion using topup. I know it can be used for one DTI scan and 2 b0 but have no experience with two scans!
Thanks in advance
Bassam
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Hi Bassam,
so there is 1 b0 volume in each phase encoding direction - hopefully 180° opposing? Then just calculate the topup distortion field with these 2 b0s and feed the result into eddy. Here you would use all of the data (b0_PED1+bXXX_PED1+b0_PED2+bXXX_PED2) of both PEDs concatenated into one file…
What you are looking for is the standard case for topup&eddy and well described on their user guide website.
Best,
Martin
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Dear colleagues.
Hello, I am currently studying brain connectivity in the disease group.
Recently, I constructed a connectivity matrix from each participants' neuroimaging data (diffusion tensor imaging), then ran the edge-wise correlational analysis with the neuropsychological score using a tool similar to Network-based statistics (a.k.a NBS; Zalesky et al., 2010).
As a result, I got an edge-level network consisting of 10 edges with 19 nodes.
Those significantly denoted edges are not connected to each other but identified as single edges.
Conventionally, I used graph theoretical measures such as a degree or betweenness centrality for defining hub node (i.e. hub region = betweenness centrality + 1SD within the nodes of the network).
However, in this case, I have an edge-level network that is hard to say is clustered or connected but consisted of multiple single edges.
From here, I want to specifically emphasize more significant edges or nodes within the identified network as a hub region (well it is hard to say it is a hub, but at least for easy comprehension), but I am quite struggling with what approach to take.
All discussion and suggestions are welcomed here.
Or if I am misunderstanding any, please give me feedback or comments.
Thank you in advance!
Jean
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For example, you could invent a measure based on cluster coefficients.
You could consider all vertices that lie in a shortest edge distance up to a certain maximum, and count all edge in it.
Also, it can be interesting to determine the number of cliques a vertex takes part in. Or the distribution of the number of vertices in these cliques. Or the overlapping percentages of any pair of cliques a vertex is in.
Regards,
Joachim
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Hi everybody.
I'm working on tractography data, computing projections from seeds to a set of targets. Since I don't compute tractography for targets, my adjacency matrix is rectangular (non-square). I need to compute graph analysis measures such as degree centrality, betweenness centrality etc. I tried with gretna, brain connectivity toolbox, braph, GAT, igraph, but they all require a square adjacency matrix.
Is there any software which can handle non square matrices, or can somehow deal with missing data?
Thank you
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It depends :
if there is no edges between targets then you have to complete missing part with zeros.
if you think there should be edges but they are missing in data, you may explore link prediction bibliography (lot of papers using graph embeddings and neural networks) ... in terms of software, many papers have their code open and I remember I saw somthing with neo4j like inf this post : https://medium.com/neo4j/link-prediction-with-neo4j-part-1-an-introduction-713aa779fd9
but I do not know much about real performance. Hope this will help.
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Very clinically relevant to reduce stress and related obesity
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Some studies show changes in the normal circadian patterns of cortisol in children with autism. Children with autism are thought to exhibit poorly regulated negative feedback from the HPA axis. Some children with autism have an abnormal daily rhythm and / or lack of cortisol suppression in response to dexamethasone, which is usually expected to elicit a severe negative feedback response. In children with autism, the negative feedback mechanism that regulates the HPA axis may be less effective, resulting in a prolonged increase in cortisol due to activation by a stress response.
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Thresholding is a common step in the preparation of a connectivity matrix for graph-theoretical analysis. This is done by applying an absolute or a proportional weight threshold, but this threshold is often arbitrarily determined. Most papers I found do use a threshold, but I saw at least a few that didn't.
  • Given a functional connectivity matrix based on EEG data (the measure used to compute connectivity is the phase-lag index (PLI), an index of phase synchronization), is thresholding an essential step? Or can graph-theoretical measures be computed also on the completely connected, weighted graph?
Some papers used a range of proportional thresholds (e.g. 10% -90% of the strongest connections, in steps of 2.5%), obtaining a graph for each threshold, then computed a graph-theoretical measure for each graph and then averaged these measures across the different graphs to get a single measure. Others, instead of a threshold, used a minimally spanning tree (MST) to retain the strongest connections.
  • If thresholding is the best practice, what is the best approach to do so? What are the pros and cons of each?
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Hi, Marta
It's very interesting topic about the threshold choice during the brain network analysis. I was also concerned this threshold problem before. But now I have read a lot of papers and got some inspirations, here I share with you!
(1) First, as Avraam answered, minimum spanning tree (MST) is an unbiased choice to avoid the threshold as the MST extract the backbone of the network and it generates the non-cycle graph with all nodes connected (also with total smallest edge distance). CJ Stam and his team have proved the MST is another alternative choice to avoid the threshold. Maybe you can search the google scholoar by using the key words "CJ Stam", "minimum spanning tree", "unbiased" or other related key words. I have also focused on the minimum spanning tree in EEG brain network and recent I am ready to submit a paper about this topic, if someday the paper was accepted and published, I can send it to your email. Rather than traditional MST, I have also given some new try in this paper.
(2) Second, maybe you can try the fully connected weighted network (rather than binary), that is no threshold used. Although fully connected network may not represent the true brain network connectivity state, however, it provides very robust classification features to distinguish the EEG resting and task states for both healthy subjects and patients. you can read my recent published paper " Changes in Brain Functional Network Connectivity in Adult Moyamoya Disease". In this paper, we adopted the fully connected weighed network rather than chose a fixed threshold, maybe you can try this way.
(3) Third, the another automatic threshold choice strategy is based on the statistical significance. I read this paper "A novel index of functional connectivity: phase lag index based on Wikcoxon signed rank test". Although it's a method related paper, it inspired me to consider using the statistical test to automatically avoid the threshold. You can download it and hope it be helpful to you.
If you still have any question about the brain network analysis, you can send your puzzle by email "gxzheng16@fudan.edu.cn". If I have spare time, and I will give your reply.
Gaoxing Zheng
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I have used the BrainViewer for MATLAB, but since i have migrated to python, I do not know any package that helps for a nice publication-quality images of connectivity.
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There are a lot of nice tools and projects:
The human Brain project https://www.humanbrainproject.eu/en/ and the connectome project http://www.humanconnectomeproject.org/
nilearn: Machine learning for NeuroImaging
freesurfer
pycortex
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Can I study brain connectivity using EEG lab ? If not, any suggestion for tools for simulation the brain connectivity ?Thank you
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Thank you for the suggestion !
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Why the functional connectivity decreases as frequency increases in resting-state fMRI studies?
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For a given TR=1sec (sampling period) then, by Nyquist we have f = 1 / (2* TR). So the frequency range in this case is 0 - 0.5 Hz.
Next, the heart rate and respiration rate are 1.3Hz and 0.2 Hz.
In human fMRI we usually use 0.01 - 0.1 Hz band pass filter to keep only the interesting frequencies and discard potential noise sources.
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Actually there are lots of Matlab toolboxes for analysis brain networks ( functional connectivity ) such as
-Brain connectivity toolbox
- GAT
- GRETNA
-Graphvar
and etc...
I have some correlation matrix (brain graphs) from different classes (patients and control ) and I want to analyze functional connectivity, extracting graph features such as small world-ness, doing statistical analysis and finally plotting graphs on a brain (atlas).
Does anybody know which toolbox is appropriate and user-friendly?
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Joint pain is a very common problem with many possible causes - but it's usually a result of injury or arthritis.
Due to the fact that anxiety can increase long term stress, the risk of of inflammation is high. I think This inflammation can cause pain and swelling in joints, affecting every day movements.
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Please take a look at the following PDF attachment.
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These kinases are key to CVD and AD and heart during aging. Later, cancer and AD's connection was my focus. The field is now very diverse, but if we understand aging we can understand all age related diseases. I think the target is a hot area. open to discuss possibilities.
Regards
Dr. Obrenovich
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Hi Mark@
I do agree with you, when you said that: if we understand aging we can understand all age related diseases .
But what do you mean by " the target is a hot area" ???
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I need a standard brain connectivity matrix (CM) for healthy and pathological conditions, such as Alzheimer's disease, Down syndrom, etc. namely, a text or binary file which has all the connection strengths between some regions of interest (ROIs). I need this matrix, in addition to the MNI map of the ROIs used in the matrix. I couldn't find any database for it. It seems that each group measures these matrices by their own, rather than referring to a general database.
Is there a general database for brain connectivity that anyone can use and refer to? A matrix for the connections and physical locations of the default mode network (DMN) would also help.
Thanks in advance for your help!
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You can see the TVB: The Virtual Brain platform
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In sleep, brain activity, specially in the default mode network (DMN) is reduced. For example, Amyloid beta production by neurons has a 70 % jump in wakefulness compared with sleep (Kress et al. 2018 J Exper Med). Can one assume that the same would happen in case of coma, or anaesthesia? Are they similar at all in terms of brain activity?
As I searched, it seems we are able to induce an artificial coma, like what we do for anaesthesia? If so, how long could it last at most?
The last but not least, have people taken effect systematic MRI during comma, anaesthesia, and sleep?
I thank you in advance for your help.
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Hi.
Yes they are differ, there are difference in the state of the brain among coma, sleep, and anaesthesia.
I recomend to read this article, it exlain the differences very clear.
General Anesthesia, Sleep, and Coma
Emery N. Brown, M.D., Ph.D., Ralph Lydic, Ph.D., and Nicholas D. Schiff, M.D.Author
Amit
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Dear Experts,
Head motion is an important confounding variable for fmri studies. I know that a range of different methods is used to correct motion in resting-state functional connectivity studies. One of the simplest methods is to exclude subjects whose head motion exceeds 1-3 mm in translation/rotation. Scrubing, frame-wise displacement are among the others. I will use dynamic causal modelling to investigate effective connectivity during a task. However, I am not sure that I should be sensitive to this topic because some of these methods are developed using resting-state data. I was wondering whether it is uselful to apply motion correction to task fmri data as well.
Best,
Seda
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There has been talk of the possibility that the human being is assisted by nano artifacts that would work at the brain level. These nano particles would significantly increase our ability to think and respond to increasingly complex tasks.
I would like to know what are the ethical arguments that move in this field
What is the current perspective of these procedures?
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Thanks for kind invitation to answer this research question. There were some similar discussions, but I am not the expert in this scientific area.
I do remember the predictions of Ray Kurzweil.
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Since there are long term treatments for some diseases or many type of cancers not knowing how to be treated, im wondering how brain activity can ever control the disease.
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Positivity (positive attitude) leads to the hope of improving the disease and in my opinion it really accelerate the speed of recovery from a disease.
For curing cancer pranayam (breathing practices) and meditation are really beneficial.
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currently i'm working on complex networks and software security. so i need a toolbox for its mapping, can anyone suggest ant good toolbox for it.
thanks
regards
Awais Akram
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There are many network analysis tool packs as Pedro rightly mentioned. However it depends on what platform you are comfortable in: MATLAB? Python? R? Linux? or do you wanna use standalone?
That way it will be easy for people to suggest options for you. BCT works well with MATLAB, but it will be more efficient if you use parallel computing. As standalone you can use Gephi.
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How to interpret sensor-level functional connectivity results (EEG)?
I got some functional connectivity results based on EEG data on sensor level. It is not practical to do source localization, cause it's only 32-channel recording. Since the signal I investigate is a steady-state response phase locked to periodic stimuli, I take coherence as functional connectivity.
My questions are:
1) Are functional connectivity results on the sensor-level accessible to information about underlying neural network? and if so, how should I interpret the data.
2) I observed a clear trend of left- / right- hemisphere lateralization with change of conditions. can i draw any conclusion about laterality based on sensor-level data? (I cannot think of any reason not to do that, since the spatial resolution of eeg, though very poor, can still reach hemisphere level.)
Thanks.
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Good question here
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Dear fellow scholars,
We are working with a specific group of projection neurons from the brainstem to the spinal cord, and we want to somehow label (genetically, immuno etc) its target neurons in the spinal cord. Have have 2 mouse lines which could be useful, one with cre expression in this group and some other cells, and one with both cre and flp expression in this group and only very few other cells. How can I map the output neurons?
Some ideas I already thought of - mostly for ex vivo preparations:
1) Stimulate the projection neurons (optogenetically, electrically etc) at high intensity to induce cFos expression in target neurons (possibly ex vivo in ACSF). To predominantly target monosynaptic outputs, mephenesin, a drug that diminishes polysynaptic transmission, or maybe change ACSF ion compositions could be done(?) Thoughts on this?
2) Certain viruses have recently been developed which do anterograde monosynaptic jumps. This has potential, but Im not sure how robust this is,and some issues are correctly targeting the neuron group with virus injection (specificity, coverage), and the need for adult animals (I prefer to study developmentally immature animals). Does anyone know any good viruses, that might be cre or flp or both specific?
3) Cre/Flp sensitive transgene mice that could be used?
Any ideas or pointers are welcome. Also criticism on my current ideas, especially cFos and polysynaptic blocking.
Best regards,
Anders
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Hi,
my first thought is pseudotyped rabies virus (idea 2). This system is very robust and used widely. However, this is mostly used in the brain. Gaining access to the brainstem to inject and subsequently have the animals survive long enough to get proper expression in the connected cells might be a challenge. I have no experience in that. This paper should get you started on this method
Idea 1 is very creative, but will require a lot of work and controls to limit the responses to only the monosynaptically connected neurons. I am not sure of the results will ever fully convince anyone as it will be very hard to limit excitation to monosynpatic connections.
crossing the mouse line with a Cre reporter line would get you reasonably close to what you want. You could label all the Cre positive cells with a fluorescent protein. Than you would have to reconstruct the axons to find out where they project to. Than the problems start. How to truly determine on which cells they terminate? Proximity is not necessarily a synaptic connection. If you do see a synaptic connection, how do you determine what cell type the upstream cell is? You would have to do immunostaining with a pre- and post-synaptic marker to establish that there is indeed a synaptic connection and stain for a marker, (somatostatin, parvalbumin,...) to determine the identity of the upstream neuron.
good luck
Nils
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brain function and its signals are known to be affected by several things. one of them can obviously be gravity. is it possible that people become smarter on another planet or even have better memory? is brain function dependent on gravity at all?
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There is a paper in Cognitive Neurodynamics, year 2017 or 2016, that describes gravitational issues in the context of neuronal activity.
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the recent researches have shown that blind people react(smile) to the smile of a person in front of them. how this connection occures?
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Professor Zeashan Khan,
Thank you very much.
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as we know the specific frequency of sound stimulate the specific part of cochlea of ear that was named cilia(hair cells). subsequently ,the cilia convert the amplitude of the sound to corresponding frequency. nevertheless, is the brain sense the rhythm of the sound?
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May not be relevant but there have been many studies of 'sonic driving' during trance experience and other situations. Personally, I was trained in facilitating shamanic trance postures using rattle by Felicitas Goodman, Spirits Ride the Wind, etc. In my search at least in English Neher 1961 was a pioneer on this topic. Goodman also mentions in her books EEG studies of practitioners of shamanic trance. Somewhere I read a study recently that sonic driving is an independent variable for trance; not necessary.
Best regards,
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Hi, I hope somebody experienced in neuroanatomy and fMRI could help me! Are the Lentiform nucleus (MNI coordinates -24 9 12) and the Cerebellum (3 -51 -27) part of the default mode network?
Thank you in advance!
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Ciao Giovanna,
Many works using ICA studied the brain funcitonal connnectivity to find cerebellar components of classical networks, and suggest a link between the DMN and the Lobule IX (https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2742620/). You may want to check the distance from your cluster.
Anyway, the interpretation of functional associations between cortical DMN and the cerebellum depends on the method, and on the experimental paradigm.
I'm less sure about the putamen. Basal Ganglia network is usually anti-correlated with the DMN, and the disruption of this anti-correlation seems to be associated with clinical synptoms (https://www.ncbi.nlm.nih.gov/pubmed/23400553).
Anyway, disrupted anti-correlation is something very common across pathologies. And most of the times we don't know what does it mean.
Hope this helps,
Simone
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If investigating the effects of a stimulus or medication on connectivity, is there a reason why resting state would be preferable over a task based analysis? Some research has shown that there are potential differences between coactivation patterns during a task and resting state correlations (Di, Gohel, Kim & Biswal, 2013). Why wouldn't investigating the effects of that stimulus/medication in the context of a cognitive task be more desirable? Should investigating both be necessary?
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Both have theoretical pros and cons, and the answer will end up being case-specific.
Task-based analyses allow a researcher to observe the response of the brain to a specific perturbation.
Pro: The ideal choice of task (if there is any) would help accentuate differences in connectivity between research groups (pro).
Con: However, the choice of task can be difficult for the given problem; the chosen task may not be best suited for accentuating desired differences (and you may not know this until you've completed the study).
In contrast, resting state analyses allow one to probe the "intrinsic" architecture of the brain. Some have suggested that infinite resting state data could encompass all possible tasks states (cf. Cole et al. for a useful discussion on this topic). Of course, there are some possible caveats to this hypothesis, e.g., some brain states may only be accessible in very extreme situations. In practice, much of functional connectivity estimated from task data (if not contrasted with a baseline) can be quite similar to that estimated from rest data—though where the dissimilarities lie may be very interesting.
Pro: No need to search through task space for ideal task; data acquired can be used for multiple research questions
Con: Might not accentuate differences of interest as much as the "ideal" task.
Cole, M. W., Bassett, D. S., Power, J. D., Braver, T. S., & Petersen, S. E. (2014). Intrinsic and task-evoked network architectures of the human brain. Neuron, 83(1), 238-251.
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Hi!
I have an ERP data. after analyzing, I found two significant effect. one is over right occipito pariteal and the other one is over left frontal  area. the components are in the same shape and time window but in different polarities (one positive and the other negative). the only difference is that the amplitude difference is higher on occipito parietal.
I wonder if I should report the effect on frontal area or not. according to my research question, I should seek the difference in both of these areas but I wonder the effect on frontal cortex is something reliable or is just under the effect of posterior changes. (specially that I'm using an average montage)
Best
Shadi
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Dear Stephen
thank you again
yes I know.
but I think there is no other way to do that. 
I used CSD, LORETA and dSPM and they all showed almost the same result. I checked the amplitudes too. there was difference between amplitudes in two regions and I know that this is not a good and complete reason by itself. but in general, I think if we put all these together, we can conclude about the difference between two clusters.
am I right? 
Shadi
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To obtain the ROI based  on the regions with significative activity in ICA (independent  component analysis) networks. This could be right?
Ps. In a task related paradigm
Thank you
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Yes, basically data-driven approach such as PCA and/or ICA can be the  basis of ROI definition if for example you wish to perform seed-based connectivity. However keep in mind that there exist also mixed approach between data-driven and prior-driven connectivity analysis (in seed-based connectivity analysis, seeds or ROI are what I call priors,maybe it is not so accurrate).
So in your case you could define ROI from ICA networks found during one condition of your task, and analyse connectivity among those ROIs in other conditions? What method do you use?
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Dear Experts,
How should I perform my analysis with probtrackx to obtain the number of voxels in the target mask that show connectivity to a seed mask? Something like the figure attached.
I have a small ROI as seed (2mm radius) and a single target mask that it is also waypoint and termination mask.
Thanks
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Hi 
If you want to use Probtrackx function from FSL, you can simply assign a seed mask and target masks (load as a text file). In the end, you can calculate number of voxels within the tracks connecting between the seed and the target using the output file. 
Here, they explain details. 
proj_thresh <list of volumes/surfaces> threshold
Best Regards,
Haemy Lee Masson
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Dear everyone,
I have used independant component analysis (ICA)  to extract resting state fmri brain networks. I have identified what some of the networks correspond to (such as visual,auditory,cognition and language) but there are some of the ICs left which I would like to identify. 
I have used the article entitled "Correspondence of the brain’s functional architecture during activation and rest" by Smith et al. and identified 10 of 23 networks of mine. 
Could you please recommend some other resource that visualizes the previously identified networks? (like the ones in above mentioned article)
Best Regards
Shiva
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If you download Smiths's "atlases" as linked to above, you can use fslcc on the atlas file and your group ICA file and get a list of spatial correlations between the volumes (IC's). This could give you some easier way to identify signal ICs from noise. And there will be noise. Smiths 2009 paper did two dimentionalities, and from the 24 one, they only identified 10 signal ICs, which sounds a lot like your analysis (and I think is quite common).
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I am developing a computational model of CA1, and I need to incorporate this parameter in the model. It has been surprisingly difficult to find within the literature. Thanks for the help!
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de Almeida et al. 2009 http://www.jneurosci.org/content/29/23/7497.full is a modeling paper that deals with that question and may have some good references.  
Have you checked out Hippocampal Microcircuits: A Computational Modeler's Resource Book http://www.springer.com/us/book/9781441909954?  If you can't afford it, a certain online LIBrary GENerously (.io) provides it.  
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Dear Colleagues,
I wonder if anyone can recommend a specific pipeline for diffusion MRI data preprocessing with subsequent reconstruction of adjacency matrices. Preferably, with step-by-step instructions and straightforward strategy for scripting/running multiple subjects.
I would also appreciate if you could suggest an optimal tractography algorithm to do this for 70-direction, b=2200 s/mm2 diffusion data.
Thank you very much in advance!
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I'd like to suggest camino, a JAVA-based toolbox for dMRI.
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Hi!! I want to analyze some brain connections using GFP detection, as a control. So anybody knows how would be the best way to do that? I was looking for the Adenovirus AAV-GFP but I am not sure if I would need another helper virus at the same time, or if is there any other approaching? And in this case, where can I get them?
Thanks in advance!!!
All the best,
Sandra
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Hi Sandra,
If you want to look at brain connectivity, I would suggest injecting AAV vectors containing synaptophysin (fused to a fluorescent protein).  That way, you can accurately visualize projections as fluorescence will be located in presynaptic terminals.  
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Memories and knowledge are acquired by mechanisms we don’t yet quite understand and mediated by limbic formations, notably the hippocampus (“old cortex”).  After a period of consolidation, they are permanently stored in the neocortex (“new cortex”).  From there, when recalled, they enter the service of any and all cognitive functions, from perception to language.  What is the most basic structural store of a memory or item of knowledge in the neocortex?  Is it a neuron?  The mitochondria within it?  An astrocyte?  A cortical “column”?  A cell group or assembly?  None of the above?   
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Many thanks to all for very interesting answers.  Although you treat it respectfully, I admit that the question was a bit misleading, if not unreasonable, because the term “minimal” implies that the structure in question is measurable by present means, which by its very nature may not be.  But the question was not aimed at identifying a quantifiable entity but at establishing a principle that seems to hold for old cortex as well as new cortex.  In neuroscience, as in any other science, the theoretical principle is key to progress (“without a theory the facts are silent,” Hayek said).
            In my opinion, the correct answer to my question is: “none of the above.”  Several of you, however, came close to the most correct answer, which in my view is what I call a cognit: a network of neuronal assemblies representing the sensory and/or motor qualities of a discrete life experience, synaptically linked together by their simultaneous occurrence in that experience.  
           Cognits are of many sizes, depending on the complexity of the memory or item of knowledge they represent.  The larger ones are widely distributed, straddling several areas of the cortex.  Cognits overlap one another profusely and share common nodes or features, whereby one neuronal assembly can be part of many memories or items of knowledge.  They are all inherently plastic, susceptible to growth and recombination with learning and life experience.  Their most important structural properties are: (a) hierarchical organization, (b), nesting, and (c) sharing of connective links and neuron assemblies.
           The evidence for the structure and dynamics of cognitive networks or cognits comes from the study of the activation of cortical areas and cells in well-structured behavioral conditions (review in Fuster, 2009):
1.  Working memory (WM) tests.  WM is the temporary activation of a cognit for attaining a behavioral or linguistic purpose.  The electrical and imaging signals of that activation confirm the distributed character of cognits and their associative (synaptic) character.  Vivid evidence of the latter is the activation, during WM, of neuronal assemblies in dispersed cortical regions that retain cross-modal information, that is, information encoded by learning the use of stimuli of more than one sensory modality (e.g., vision and hearing, vision and touch).
    Gibson,J.R. and Maunsell, J.H.R. (1997). Sensory modality specificity of neural activity related to memory in visual cortex. Journal of Neurophysiology 78: 1263-1275.
    Hadjikhani,N., and Ronald P.E. (1998). Cross-Modal transfer of information between the tactile and the visual representations in the human brain: A positron emission tomographic. Journal of Neuroscience 18: 1072-1084.
    Fuster,J.M. et al. (2000). Cross-modal and cross-temporal association in neurons of frontal cortex. Nature 405: 347-351.
     Zhou, Y.D. and Fuster, J.M. (2000).  Visuo-tactile cross-modal associations in cortical somatosensory cells. PNAS, 97:9777-9782.
2.  Formation and association of cognits by temporal approximation of their contexts.  In rodents, where spatial navigation has high survival value and where the hippocampus integrates cognitive functions that in the primate will be integrated by the neocortex, two important cognit properties have been upheld: (A) Time is of the essence for the formation of synaptic links between cognits, and thus for the formation and generalization of higher-level cognits. (B) Within those hierarchically higher cognits there is substantial overlap of the constituent cognits.
    Fuster J.M. - Cortex and memory: emergence of a new paradigm (2009). Journal of Cognitive Neuroscience, 21:2047-2072.
    Cai, D.J.  et al. (2016).  A shared neural ensemble links distinct contextual memories encoded close in time.  Nature, May 2016.
3.  Of course (re. Arnold’s answer), all those changes can occur unconsciously or preconsciously.
Cheers and kind regards.
Joaquin   
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people with autism often have visual avoidance problems, use stimming, have balance and postural anomalies etc as well as speech delays. When we consider that with a computer we can adjust a picture with brightness AND contrast controls. Now we know the eye can adapt to different levels of brightness, but how does the eye/brain connection work the contrast control. If a scene has low contrast then there is a lack of data to attract attention which would consequently lead to reduced cognition and this simple concept explains any of the apparent symptoms of autism. Any thoughts on this would be appreciated.
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it is hard to anwer this since when you know one case you only know ONE case. But hypersensivity is a comorbidity and therefore some subjects might have light sensivity issues, but eye contact is a totally different matter. It is not yet clear why. Is it lack of social skills, is it anxiety for being invaded by the other through the eyes (to open up), is it lack of interest in the other, shyness, absorbtion by linguistice activity... many things have been suggested and/or recorded. It can be trained as part of a coping strategy, so I don't think it is physical. I believe it is cognitive and the question I would ask is if how ASD subjects feel about being looked in the eyes (fMRI or so). I guess that to find out why, one needs to find out how they experience the mirrored image themselves. Most subconscious motivation is based on personal preference. I'm just thinking while typing. So don't take it for granted.
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Philosopher Merleau-Ponty claimed that the feeling Self is the living body or its flesh. In this case, a conjecture can be made that siamese brothers or sisters would not have opposite feelings simultaneously (as one being happy and the other sad). Do you know any data that could help to support or disconfirm this conjecture?
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Dear Alfredo,
There are no many EEG studies on Siamese twins.
We find the following:
H.G. LENARD AND F.J. SCHULTE. Polygraphic sleep study in craniopagus twins. Journal of Neurology, Neurosurgery, and Psychiatry, 1972, 35, 756-762 (however it is sleep, and twins are very small - may be self is not developed yet...)
To check your hypothesis it is interesting to contrast EEG of twins joint at somewhere in the body with EEG of twins joined at the head (craniopagus). Then again the effects may be different depending on which part of the brain is shared...
Check please the following link for the popular article and the video on twins joined at the head.
Several years ago we watched British documentary about adult twins (females) joined at the head (if we remember correctly they sheered frontal lobe). Nothing was told in this film about EEG... But what was interesting that they had different hair stiles and very different personalities. Each of them had her own room to rest with different stiles...
If you will find something please share it with us.
Best,
Alex & Andrew
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Bilinguals have been shown to perform better in a vast pallet of tasks hinting at having a better developed and more effective executive control network
The advantages range from superior inhibition of irrelevant information, enhanced decision making to faster problem solving and shifting between mental sets and even improved creativity. 
Because everything in the brain is connected and for bilinguals has been shown that they can compensate and use some are differently or more efficiently (less activation-better performance), we would like to know if this extends to the field of salience perception.   ??
Typically, the area investigated and involved in salience guided attention is posterior parietal cortex (PPC). LEFT is critically involved in attention for low-salience stimuli in the presence of highly salient distractors and the RIGHT one is involved in attending to more salient stimuli.
We would really appreciate any suggestion on how an experimental paradigm could be created using TMS or tDCS to test whether bilinguals are better (faster, more accurate) at a global-local salience task.
Please do not hesitate to PM and ask any further questions! Thank You in advance and thanks for this lovely scientific community!
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Dear Jure,
 To answer your very interesting question, here is a sketch of a study:
Basic assumptions:
 1.   What you call salience perception is the same as, or close to, perceptual attention, whether bottom-up or top-down.
2.   All selective attention, perceptual or executive, has two major components, one intensive (focus, concentration) and the other exclusionary (inhibitory control of interference or distraction).
3.  The prefrontal cortex serves both components, the dorsolateral cortex primarily the first, the inferolateral (orbital) cortex the second.
4.  All attention makes use of cognitive networks (cognits) of established memory and knowledge in the cerebral cortex.
5.  Cognitive networks with linguistic associations are mainly, but not exclusively, distributed in the cortex of the left hemisphere.
6.  Bilingual individuals, and polyglots, have wider and better connected networks, especially semantic networks, than monolingual individuals.
 Methods:
 1.  As independent variables, use anodal tDCS of frontal cortex, right, left, and bilateral.
2.  As dependent variables, take measures of performance and reaction time of (a) working-memory tasks to test executive selective attention to perceptually elicited internal representations, and (b) Stroop task or equivalent to test inhibitory control of interference and irrelevancy.
Predictions:
1.  Frontal stimulation will enhance performance of both types of tasks.
2.  Left and bilateral stimulation will be more effective than right stimulation (?).
 3.  Reaction times in both types of tasks will be shortened by stimulation.
 For more background, you may consult my publications and those of others in RG. 
 Cheers and good luck.  Joaquín.
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in Analysis of Brain Connectivity , we say Three types of connectivity are used to describe the interactions of neuronal networks: structural, functional and effective connection .
my question is : what is difference between Different types of connectivity in weight matrix and neuron connectivity? and is the fact of brain these 3 connectivity?
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Hi Zahra,
Structural (or anatomical) connectivity refers to the existence and structural integrity of tracts connecting different brain areas (i.e. white matter tracts connecting cortical areas/nuclei). Functional and effective connectivity are neuroimaging terms. While functional connectivity only refers to statistical dependence of the signal from different areas (that are assumed to me structurally connected), effective connectivity brings in the element of causation (i.e. a signal, activation in one area directly causes a change or signal, activation or depression, in another area).
Hope this helps,
Tomas
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Specifically I am interested in understanding above which value I should start suspecting that coherence has been actually inflated by other factors.
Thanks,
Eduardo 
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For each frequency, the value of coherence by itself reflects nothing more than the fraction of common "signal" registered in two locations, relative to some measure of total EEG power ("signal" + "noise")  at the same frequency. Usually coherence values  span the whole 0-1range and systematically decrease with the distance between electrodes. Therefore one cannot just consider a value of, say, 0.8 as high, and a value of 0.3 as low. Among other non-physiological factors (e.g. choice of the reference of EEG montage) coherence values mostly depend on locations of the electrodes and the frequency band. E.g. a value of 0.8 or higher is normal for the alpha-band coherence between left and right occipital EEGs, and a value of 0.3 is normal for the beta-band EEG.
All of this is to say that absolute values of coherence do not mean much and trivially reflect basic spatial and spectral properties of EEG. However what might be interesting is how coherence CHANGES due to some experimental manipulation, or how it DIFFERS between groups of subjects. This approach is being used in most studies of EEG coherence.
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In an EEG study, I am trying to compare the brain networks of 3 motor tasks while functional interactions are measured by 3 estimators coherence (Coh), imaginary part of coherence (iCoh) and partial coherence (pCoh). My results show that for task 1, more connections are achieved by Coh (denser network), for task 2 more connections are achieved by iCoh and for task 3 more connections are achieved by pCoh. Regardless of other steps and methodological considerations of this research, is there any explanation for this behavior of connectivity estimators? Is there any reference regarding this issue?   
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Thanks for your answer.
Brain connectivity estimators measure the pattern of interactions (functional/effective) in the brain.  https://en.wikipedia.org/wiki/Brain_connectivity_estimators
There are several connectivity estimators and in my question I asked about the behavior of Coh, iCoh and pCoh. Since I am dealing with 3 motor tasks with similar nature, I expected to see for example more connections are obtained by Coh for all 3 tasks compared to the network estimated by iCoh or pCoh. However, I've seen something else as explained above. I couldn't find any reference regarding such issue.    
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I converted some EEG time series to their related visibility graphs, now I need to find correlation between each 2 graphs, please note that there is no any interlink between each 2 graphs hence I'm not sure if it is possible to use some synchronization methods of complex networks.
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It depends. Are the periods iqual? If the time series has some similar trend, then its better to remove the trend at first. Are the time series stationary? Did you make an unit root test? I think it is appropriate to make also a co-integration test, in case they are not stationary.
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Miguel Nicolelis and Ronald Cicurel claim that the brain is relativistic and cannot be simulated by a Turing Machine which is contrary to well marketed ideas of simulating /mapping the whole brain  https://www.youtube.com/watch?v=7HYQsJUkyHQ  If it cannot be simulated on digital computers what is the solution to understand the brain language?
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 Dear friends, Dorian, Dick, Roman, Mario, et al.,
            There is hope (Haykin and Fuster, Proc. IEEE, 102, 608-628, 2014).  But first we have to modify our computer and some of our traditional ideas about the brain. With respect to both, here I offer humbly some of my views after half a century of working in cognitive neuroscience.  I shall be brief and cautious.  For an account of empirical evidence, read my “Cortex and Mind”  (Oxford, 2003).
            1.  Alas, the computer cannot be only digital, but also must be analog.  Most all the cognitive operation in the brain are based on analog transactions at many levels (membrane potentials, spike frequencies, firing thresholds, metabolic gradients, dendritic potentials, neurotransmitter concentrations, synaptic weights, etc., etc.). Further, the computer must be able to compute and work with probabilities, because cognition is largely probabilistic in the Bayesian sense, which means that our computer must also have a degree of plasticity.
            2.  The computer must also have distributed memory.  In the brain, especially the cortex, cognitive information is contained in a complex system of distributed, interactive and overlapping neuronal networks formed by Hebbian rules by association between temporally coincident inputs (i.e., sensory stimuli or inputs from other activated networks).  The cognitive “code” is therefore essentially relational or relativistic, and is defined by connective structure, by associations of context and temporal coincidence.  That is why, theoretically, connectionism and the connectome make some sense.
            3.  It is true that the soma of a neuron contains “memory”: in the mitochondria.  But that is genetic memory (what I call “phyletic memory,” memory of the species), some of which was acquired in evolution.  It is important for brain development and for the function of primary sensory and motor systems.  It is also important for regeneration after injury.  Further, it is the ground-base on which individual cognitive memory will be formed. But the latter consists of more or less widely distributed cortical networks or “cognits” (J. Cog. Neurosci. 21, 2047–2072, 2009).  These overlap and interact to a large extent, whereby a neuron or group of neurons practically anywhere in the cortex can be part of many networks, thus many memories or items of knowledge.  This is trouble for the connectome which, if ever comes to fruition, will be vastly more complex than the genome.
            4.  Our present tools to define the structure, let alone the dynamics, of the connectome appear rather inadequate to deal with those facts and hypotheses.   Consider DTI (diffusor tensor imaging), one of those tools presently in fashion and widely used to trace neural connections.  It is based on the analysis of the orientation of water molecules in a magnetic field.  Therefore, it can successfully visualize nerve fibers with high water content, such as myelinated fibers and some large unmyelinated ones.  But the method (I dub it “water-based paint”) is good for tractography, for visualizing large, fast conducting fibers, but not for the fine connective stuff that defines memory networks.
            5.  What’s more, those networks change all the time, even during sleep.  In sum, it is difficult to imagine a dynamic connectome that would instantiate the vicissitudes and idiosyncrasies of our thinking, remembering, perceiving, calculating and predictive brain.
            Some of this may be wrong.  But that’s the way I see it, and may be useful to model the real brain.  Cheers, Joaquín
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Is there any relationship between DNA methylation and synaptic plasticity in the brain?
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The attached publications are just a few examples of literature on the role of DNA methylation in memory, as it's been shown to strongly contribute to memory-related plasticity and LTP particularly in the hippocampus, but in general, DNA methylation has been implicated in synaptic plasticity associated with a wide variety of topics from aging to addiction to psychosis.
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I need a file that I can download use for data analysis (basically automatically determining what vascular distribution a stroke has occurred in).
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You may be interested in these 4 entry points. Contacting these authors should narrow your search.  Be also aware of the variability of the vascular territories.
I am also looking at some similar material.
Ref1.  probabilistic map produced by Michel Dojat in Grenoble france (GIN, Grenoble Institut des Neurosciences INSERM : U836). The digital Atlas of the Blood supply territories of the brain (BST), derived from the 12 printed serial sections in the axial plan developed by Tatu et al. The atlas fits the Talairach space, this 3D atlas is used to determine the stroke subtype
see : Yacine Kabir, Michel Dojat, et al. Multimodal MRI segmentation of ischemic stroke lesions. Conf Proc IEEE Eng Med Biol Soc. 2007 ; 2007: 1595–1598
Ref. 2: the digital probabilistic map of PCA infarcts produced by Thanh G. Phan et al (Digital Map of Posterior Cerebral Artery Infarcts Associated With Posterior Cerebral Artery Trunk and Branch Occlusion - Stroke 2007;38;1805-1811). They register their PCA lesions with the MNI template. It is not a vascular territory map but a map of probability of stroke.
Ref. 3: Another probabilistic map for ICA produced by Jae Sung Lee et al in Seoul.: Probabilistic map of blood flow distribution in the brain from the internal carotid artery. NeuroImage 23 (2004) 1422– 1431.
Ref. 4: Analysis of ischemic stroke MR images by means of brain atlases of anatomy and blood supply territories. W Nowinski et al. Academic Radiology. 13, 8, Pages 1025–1034, August 2006. Their approach is a atlas-to-scan transformation (mapping), quite the opposite of what you may want to do (mapping each scan to the registered atlas).
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Hi guys,
I am using the Brain Connectivity Toolbox (BCT) to do some network analysis on my resting fmri data. But I encountered a serious problem during using 'modularity_finetune_und_sign'. From the introduction of this command, I found out it should be used this way: [Ci Q] = modularity_finetune_und_sign(W,'sta'), W should be the input matrix, 'sta' is the option, and the Ci & Q are output.
My input is a 90 by 90 matrix, with diagonal set to 0s, as required. If I directly run the command, I encounter an error :
Error using ==
Matrix dimensions must agree.
Error in modularity_finetune_und_sign (line 117)
Q0 = (W0-(Kn0*Kn0.')/s0).*(m==m.');
Then I decided to open the script of the command, and executed it step by step.
1 n=length(W); %number of nodes/modules
2 if ~exist('qtype','var') || isempty(qtype);
3 qtype = 'sta';
4 end
5 if ~exist('M','var') || isempty(M);
6 M = 1:n;
7 else
8 [dum dum M] = unique(M(:).'); %align module indices
9 end
10 W0= W.*(W>0); %positive weights matrix
11 W1=-W.*(W<0); %negative weights matrix
12 s0=sum(W0(:)); %positive sum of weights
13 s1=sum(W1(:)); %negative sum of weights
14 Knm0=zeros(n,n); %positive node-to-module degree
15 Knm1=zeros(n,n); %negative node-to-module degree
16 for m=1:max(M) %loop over modules
17 Knm0(:,m)=sum(W0(:,M==m),2);
18 Knm1(:,m)=sum(W1(:,M==m),2);
19 end
20 Kn0=sum(Knm0,2); %positive node degree
21 Kn1=sum(Knm1,2); %negative node degree
22 Km0=sum(Knm0,1); %positive module degree
23 Km1=sum(Knm1,1); %negative module degree
24 switch qtype
25 case 'smp'; d0 = 1/s0; d1 = 1/s1; %dQ = dQ0/s0 - dQ1/s1;
26 case 'gja'; d0 = 1/(s0+s1); d1 = 1/(s0+s1); %dQ = (dQ0 - dQ1)/(s0+s1);
27 case 'sta'; d0 = 1/s0; d1 = 1/(s0+s1); %dQ = dQ0/s0 - dQ1/(s0+s1);
28 case 'pos'; d0 = 1/s0; d1 = 0; %dQ = dQ0/s0;
29 case 'neg'; d0 = 0; d1 = 1/s1; %dQ = -dQ1/s1;
30 otherwise; error('qtype unknown');
31 end
32 if ~s0 %adjust for absent positive weights
33 s0=1;
34 d0=0;
35 end
36 if ~s1 %adjust for absent negative weights
37 s1=1;
38 d1=0;
39 end
40 f=1; %flag for within-hierarchy search
41 while f, f=0;
42 for u=randperm(n); %loop over all nodes in random order
43 ma = M(u); %current module of u
44 dQ0 = (Knm0(u,:)+W0(u,u)-Knm0(u,ma)) - Kn0(u).*(Km0+Kn0(u)-Km0(ma))/s0; %positive dQ
45 dQ1 = (Knm1(u,:)+W1(u,u)-Knm1(u,ma)) - Kn1(u).*(Km1+Kn1(u)-Km1(ma))/s1; %negative dQ
46 dQ = d0*dQ0 - d1*dQ1; %rescaled changes in modularity
47 dQ(ma) = 0; %no changes for same module
48 [max_dQ mb] = max(dQ); %maximal increase in modularity and corresponding 49 module
50 if max_dQ>1e-10, f=1; %if maximal increase is positive (equiv. dQ(mb)>dQ(ma))
51 M(u) = mb; %reassign module
52 Knm0(:,mb)=Knm0(:,mb)+W0(:,u);
53 Knm1(:,mb)=Knm1(:,mb)+W1(:,u);
54 Knm0(:,ma)=Knm0(:,ma)-W0(:,u);
55 Knm1(:,ma)=Knm1(:,ma)-W1(:,u);
56 Km0(mb)=Km0(mb)+Kn0(u);
57 Km1(mb)=Km1(mb)+Kn1(u);
58 Km0(ma)=Km0(ma)-Kn0(u);
59 Km1(ma)=Km1(ma)-Kn1(u);
60 end
61 end
62 end
63 [dum dum M]=unique(M(:).'); %realign module indices
64 if nargout==2 %compute modularity
65 m = M(ones(1,n),:);
66 Q0 = (W0-(Kn0*Kn0.')/s0).*(m==m.');
67 Q1 = (W1-(Kn1*Kn1.')/s1).*(m==m.');
68 q = d0*sum(Q0(:)) - d1*sum(Q1(:));
69 end
In this case, my input is W (90 by 90 matrix), and option 'sta'. All the commands a executable until line 62. At then, m=90, and M was a 1 by 90 vector.
If I just skipped the line 63, then I will go to the 65 since I did have 2 outputs therefore the result of line 64 is true. By executing the line 65, m became a 90 by 90 matrix and everything followed were fine!!!
But the question is, actually, line 63 is unavoidable. However, if this line was executed, M became a 90 by 1 vector, and by executing the line 65, m became a 90 by 1 vector too!!! This will cause the previous error when executing m==m.',and of course, the m.' is a 1 by 90 vector.
So the thing is, I am sure a lot of people can execute the modularity_finetune_und_sign, however, to me, there seemed to be a problem unfix-able unless I change the original script of that command!
Could anyone help me?
I sincerely appreciate that!
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Dear Xiao-Song, I think it has to do with the new behavior of the "unique" command in Matlab. They indeed changed its behavior in the latest releases (starting after R2012b), and when testing the BCT code I indeed have the same issue as you. You might want to change the line 
[dum dum M]=unique(M(:).');
with
[dum dum M]=unique(M(:).', 'legacy');
to preserve the "original" meaning of this line. See the legacy paragraph in:
Cheers,
David
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i want to check antioxidant activity on brain , so is it feasible to use zebra fish in place of animal model 
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Yes, you can screen anti-oxidant activity in Zebra fish 
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We currently use the NBS (network-based statistics) method by Zalesky et al (2010), which corrects for the mass-multiple comparisons, while taking into consideration the internal structure of the connectivity networks and enables GLM with permutation test. Another option is, of course, to perform edge-level comparisons with FDR afterwards. Any other suggestions?
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Andras, 
Thank you for your reply. I agree that small case numbers and huge number of features is not optimal no matter what approach you take, but there are a few ways to mine that data in an unbiased way to find meaningful relationships. Here is what I mean (in response to your concerns):
"Having a million observations ("features") per patient will always lead to optimal classification, as that is the criteria of the training"
I am not sure what you mean by optimal, but if you are doing Leave One Out (LOO) prediction, then it makes no difference how many samples you have, you should not identify spurious correlations by chance. That said, having a million observations, the vast majority of which are noise, will in many cases make it less likely that a machine learning algorthing (MLA) will seperate your groups with high accuracy.  This point is addressed below.
"I do not see how that would help to tackle the huge multiple comparison problem (maybe 1000*1000 non-independent observations)"
Doing principle component analysis on your correlation matrix (or independent component analysis on your timecourses) before hand enables you to greatly reduce the number of observation that you are giving to your MLA. Functional connectivity matrices are highly degenerate (http://www.pnas.org/content/96/6/3257.full). You may have a million values, but they are not independent samples and there is much mutual information. In other words, you can capture almost all of the information in that million connections using maybe 40 components. 
So the approach that I take to group differences is to run PCA on the FC matrices across all of my samples, and then plug the components in to a machine learning algorithm to determine separability and identify meaningful features (as mentioned above). If no real group difference exists, then no separability will be found.  I can't reference any of my own publications on this matter (give me a few months on that), but the Dosenbach 2010 paper was my jumping off point. Does that adequately address your concerns?
Josh
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I am looking at cFos activation in the brain, and have a few areas of interest. I am also looking at left/right lateralisation in each area.
I have 3 groups (naive, vehicle, test) with uneven sample sizes. For each animal I have an average value per area, left and right hemispheres (cells/area).
I am thinking I should do a 2-Way ANOVA (using group and left/right as factors), but the software I use (OriginPro) won't let me do this analysis with different group sizes.
Any thoughts?
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Use SAS:
 
PROC GLM DATA=yourdata;
class group left_right;
model average_value=group left_right;
run;
You simply need to replace the bold characters with your real variable names. Let me know!
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I wonder if anyone has ever experienced running graph theoretical (GT) analysis for task fMRI. If so, what are the general recommendations, precautions, pitfalls?
For instance, given that for this kind of analysis long sessions are strongly recommended, do you suggest to acquire images during a particular task within one large block, or do you think it is still better to use a regular block design, then "cut" the data and reconstruct connectivity matrices for different conditions separately? What would be the recommended block length then?
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I think the co-activation network across participants might be the most simple and direct approach. This method seems to apply to any fMRI design.
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We recently posited that interactions within/among unconscious processes could serve as a bridge between conscious and unconscious modulations on each other. From the viewpoint of conscious and unconscious process, the three points (e.g. the unconscious modulation on conscious processes; the conscious modulation on unconscious processes; the interactive influence within/among unconscious processes, which is seldom investigated by now) might form a complete set of cognitive mechanism in brain research and serve as a candidate way of integration.
What’s your opinion?
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Dear T. Kutsenko, You probably have a good point in regard to etymology, but would you agree that the feeling is more fundamental to consciousness than the common knowledge? Mirror neuron systems can operate without consciousness; at least they can be instantiated in non-conscious machines. The theory of mind (or one of its variants) implies the existence of a degree of consciousness: why should someone care about other people´s mental states if there is no feeling about the other persons?
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Only few papers actually explain the mathematics of connectivity they've used.
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Functional and effective.
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We are going to use motor imagery based BCI for stroke rehabilitation and currently I am going through some papers that discuss the EEG-EMG coherence and time lag. While the results of different studies are not quite consistent and you can see values around 50 MS for LFP-EEG time lag in monkeys (Morrow and Miller, 2003; Rivera-Alvidrez et al., 2010), you will see conduction time measured by TMS around 20 ms(Samii et al., 1998), and finally EEG-EMG lag time of around 26 ms (Whitham et al., 2011).
Could someone please let me know how to interpret these differences?
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Dear Sir
It is of a limited use, because this pathway include may sites; cortical part and extra-cortical part. I tried to evaluate this method in 1996 in which we studied the pathway of reaction time by starting with visual stimulus and as the patient to do a response. During this procedure we calculate, then the EEG waves with movement then the EMG. Our conclusion that this is not so useful.
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.
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Tin-eye. close, but not bang-on. Sorry it couldn't help.
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I want to use BrainVoyager for resting state analysis. Specifically, I would like to do temporal correlations between two ROIs and/or functional connectivity maps from seed regions. I have read some papers that used BV for this type of analysis, I have read threads on the BV websites and I believe that I understand the topic theoretically. I am not able to figure out how exactly to proceed in the program. Any help will be greatly appreciated!
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Hey,
after defining Rois. You can also sample the time course (rtc) from multiple functional files (vtc) at the same time. Thus if you would like to extract the timecourse from a region(e.g. group analysis) for several runs (or subjects) this can be done by adding vtcs in the roi analysis box (See Matthew's response above). this will produce a txt file with all timecourses for all vois selected in the list and can be done for several functional files together. I later parse these in matlab to analyze roi to roi correlations and create rtc. files to use as predictors for FC maps. i have a few scripts than can be used if you need them contact me.
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The Economist magazine (Aug. 3, 2013) published the following statement in the article entitled ‘The Machine of a New Soul’:
“An important property of a real brain is that it is what is referred to as a small-world network. Each neuron within it has tens of thousands of synaptic connections with other neurons. This means that, even though a human brain contains about 86 billion neurons, each is within two or three connections of all the others via myriad potential routes.”
Does anyone have a reference for the statement ‘each [neuron] is within two or three connections of all the others via myriad potential routes’?
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In the past, atrophy of brain cells was considered untreatable, they could not be regenerated.
But with epigenetics, perhaps the gene expression for growth of new brain cells can be activated. What are your thoughts?
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I've been always interested in epigenetics, mainly in cardiovascular cells, but, as I encountered cerebellar disorders, linked to primary cilia dysfuntion, I had a dream, that is linking primary cilia signalling to gene expression and epigenetics in cell and animal models of ciliopathies. There are no reports in literature, but as some ciliary protein may translocate to the nucleus, and some pathways pivotal to neurogenesis are altered in these pathological conditions, I believe that the epigenetic profile (in terms of histone modifications, miRNA expression, activity of chromatin remodelling enzymes) of patients affected by cerebellar disorders is altered and restoring a correct gene expression network by EpiDrugs treatment may help to partially revert the phenotype. I'm working on this and I have some interesting data but I have still no papers out. I don't know if my opinion may help..
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I need resting state multi channel EEGs or fMRI recordings from people with bipolar depression and healthy control
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Dear Cheryl,
Thank you for your kind attention
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We are about to get the necessary materials to do CLARITY with rat brains, and wonder if anyone else here has given it a shot. I figured this might be a good place to share any pitfalls we might come across, etc. I don't anticipate any problems at the moment, as the protocol is very clear and detailed. Very excited to be trying CLARITY out.
Here is a link to the CLARITY protocol: http://clarityresourcecenter.org/
Chung, K., J. Wallace, S.-Y. Kim, S. Kalyanasundaram, A. S. Andalman, T. J. Davidson, J. J. Mirzabekov, K. A. Zalocusky, J. Mattis, A. K. Denisin, S. Pak, H. Bernstein, C. Ramakrishnan, L. Grosenick, V. Gradinaru, and K. Deisseroth. 2013. Structural and molecular interrogation of intact biological systems. Nature advance online publication (April). http://www.nature.com/nature/journal/vaop/ncurrent/full/nature12107.html
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I've been thinking about trying the CLARITY method myself since Deisseroth's paper came out. I'd love to find out how well it works and some of the pitfalls you encounter Will. Thanks for taking the initiative and sharing your experience with the neuroscience community. If you'd like to collaborate or work on it together, I'd be happy to do so, seeing that I'm not too far from where you are.
By the way, do you think a CLARITY kit would be commercially available soon, just like Scale solution? He has got to be in the process of commercializing it either himself or in partnership with a company like Olympus or Invitrogen. However the price of the kit would probably be much higher than the sum of all parts on Deisseroth's materials list.
In the spirit of sharing, I've been using Scale solution for a year now clearing brains that had been IUEd. Scale works great and is, like you said, economical. Don't buy it from Olympus, since they charge something like $300+ per liter, when you can make it yourself at a fraction of the cost. Just remember to use DI water, otherwise it may not work. Scale works really well on thick brain slices, such as those used for ephys. We can image through and through a 4oo um slice on a confocal LSM with ease. On whole brains it works fairly well but takes much longer. We tend to hemisect the brain before clearing. This not only helps with clearing efficiency, it gives you more flat surfaces on which to orient your brain during imaging. Here are the down sides to Scale. 1) On a whole or hemisected brain, optical sectioning of deep brain structures is still not ideal. Resolving fine structures such as axons and fine dendrites is still difficult, presumably due to diffraction limits. Even fluorescent somata are sometimes difficult to resolve cleanly in whole brains cleared for 2 weeks, if they are too densely packed such as in the rostral migratory stream. 2) The cleared tissue swells approximately 20-30% so everything looks a little bigger, making absolute measurements of cell parameters impossible. Relative comparison between tissues cleared in Scale is okay. 3) While gray matter clears beautifully, white matter such as the corpus callosum is hard to clear. Tracking a single labeled axon across the corpus callosum in a thick slice cleared in Scale is not feasible (at least in our hands). 4) Once cleared in Scale, the tissue needs to stay in Scale or the opacity will revert, which means submerging water immersion objectives in high concentration of urea in order to image in magnifications larger than 20x. We don't know if this shortens the life of the water immersion objectives.
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I know econnectome and SIFT but these only seem to offer single subject analysis.
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The software DigEEGXP by Cornelis J. Stam computes the so-called Synchronization Likelihood that is a great measure of linear and nonlinear connectivity between different EEG channels.
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Fractals are the products of reiterative processes. The product of a simple multiplication is fed into a new operation. As a result, self-similar structures appear. This can be observed in many living organisms, where the splitting and continuous reproduction of cells generates self-similar patterns (cardiovascular system, neural networks, structure of the lung, trees, their branches and their leaves, etc.) As a linguist I am interested in the possibility of describing language as a fractal. Inasmuch the brain IS a fractal, language has also a chance of being one.
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Hi,
I think we should refrain from absolute statements like : "brain IS a fractal" or "language IS a fractal". In history of science it is very common to transfer some current (an trendy) concepts from different fields to explaining human behavior. Descartes famously describe humans as machines because that was the useful framework at his times. Currently commonly people think about processing in brain using computer analogy. More higher level models included "catastrophe theory" and fractals, random matrix, various network theories etc.. to explain function of brain and human behavior.
Each of this frameworks may explain some phenomena similarly as Bohr's planetary model of atom explains many experimental results. However, no physicist would say (even Bohr) that atom IS a planetary system.
The same should be said for application of any abstract model to explain any of the brain function.
So fractal theory may be useful to provide some framework for language, However, neither brain nor language IS a fractal.