Science method

Diffusion Tensor Imaging - Science method

The use of diffusion ANISOTROPY data from diffusion magnetic resonance imaging results to construct images based on the direction of the faster diffusing molecules.
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Dear DTI Experts,
I have run the following TBSS pipeline in order to get a whole-brain FA skeleton for a cohort of 4-8 year old children:
#Run TBSS Preprocessing (must navigate to the folder with all subjects' FA maps)
tbss_1_preproc *.nii.gz
#TBSS Nonlinear registration: Register to standard space
tbss_2_reg -T
#Get study-specific FA skeleton.  Run from TBSS directory (directory before FA folder)
tbss_3_postreg -T
#TBSS Threshold for FA values <.2
tbss_4_prestats -0.2
As part of my resultant output, I receive a whole-brain standard space mean FA skeleton mask.  How should i go about getting my whole-brain scalars (FA, MD, AD, etc.) using this output?  Is it acceptable to warp the standard space mean FA skeleton mask to each subjects' diffusion space, and then overlay the resultant skeleton on each scalar to get whole-brain metrics for each participant?  Please advise.
Kind regards,
Linda
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@Linda Hoffman You can read the User Guide on the website of TBSS to get other scalars (MD, AD RD):
Using non-FA Images in TBSS
It is straightforward to apply TBSS to other diffusion-derived data than FA images. For example, you may be interested in how MD (mean diffusivity) or the first diffusion tensor eigenvalue varies between different subjects.
To achieve this we recommend using the FA images to achieve the nonlinear registration and skeletonisation stages, and also to estimate the projection vectors from each individual subject onto the mean FA skeleton. The nonlinear warps and skeleton projection can then also be applied to other images such as the second eigenvalue. The following instructions assume that you want to run TBSS on the second eigenvalue, named L2 by dtifit:
  • Run the full TBSS analysis (see all steps above) on your FA data.
  • Create a new directory called L2 (or any other name) in your TBSS analysis directory (the one that contains the existing origdata, FA and stats directories from the FA analysis). Type: mkdir L2
  • Copy your L2 images into this new directory, making sure that they are named exactly the same as the original FA images were (look in origdata to check the original names - and keep them exactly the same, even if they include FA, which can be confusing; e.g. if there is an image origdata/subj005_FA.nii.gz then you need an image L2/subj005_FA.nii.gz and this file should contain the L2 data, even though it has FA in the name).
  • Now, making sure that you are in your top working TBSS directory (the one that now contains FA, stats and L2 subdirectories) and run the tbss_non_FA script, telling it that the alternate data is called L2. This will apply the original nonlinear registration to the L2 data, merge all subjects' warped L2 data into a 4D file stats/all_L2, project this onto the original mean FA skeleton (using the original FA data to find the projection vectors), resulting in the 4D projected data stats/all_L2_skeletonised. Run: tbss_non_FA L2
  • You can now run voxelwise stats on the projected 4D data all_L2_skeletonised in the same manner as described above.
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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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Hi, I am conducting a Diffusion Tensor Imaging study on three groups of subjects, first-episode psychosis (FEP) group (Group 1), their siblings (S) (Group 2), and control group (C) (Group 3). I would like to infer the differences of their diffusivity values. I'm using TBSS to do this. I will also use F-Test and then T-Tests as post-hoc. However, I do not know how I can design my matrix in GLM because I have different sample sizes in my 3 group such as 19 FEP, 20 S, and 21 C. Could you help me on this issue, please?
Thank you so much,
Tugce
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This would be a great question to post in our new free medical imaging question and answer forum ( www.imagingQA.com ), there are a number of DTI experts in the community. If useful, please feel free to open a new topic at the link below :
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Hey,
I am currently working on a project where I am analysing narcissism features and white matter
integrity.
I did multiple comparsions (multiple regression) because I was looking for correlations for three tracts, a few sub scales o a questionnaire and positive as well as negative contrasts.
Now I have to correct for multiple comparisons. Is there any other method than bonferroni which is more robust?
#FSL
#DTI
#multiple comparisons
#correction
#multiple regression
#brain imaging
#diffusion tensor imaging
#Fractional anisotropy
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FDR is more robust.
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I have one DTI dataset, and I've applied two different reconstruction algorithms to it. Thus there were two datasets with the same anatomical structure but varying in sth like the artifacts and SNR. I want to generate their FA maps. I use EDDY and DITFIT from FSL. I find that the results of eddy correction are different. That is, the transformations of two datasets are not the same, and the shape of the corrected images are also different.
eddy_parameters is one of the output files of EDDY. It contains the parameters of the transformation. How can I apply the eddy_parameters of one dataset to the other to ensure the same correction results?
The eddy_parameters file contains a matrix with a size of 21*16. There are 21 volumes in the DTI dataset, which means there are 16 parameters of transformation for each volume. But I don't know what each parameter exactly means? Does anyone have experience in this?
Thank you very much!
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Does anybody know any software which can convert pictures from MHA format to NIFTI or DCM format?
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i think that the MRIconvert is the best tool
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Hi I am currently working on my masters thesis and could really use some help. The project includes comparing FA and MD values to sleep EEG values. I'm currently stuck on creating the script for extracting the FA and MD. E.g. if I want to extract the FA for all participant from the genu corpus callosum tract (we're using the JHU atlas and the index number is given is 3). what kind of script would that be?
We're only looking at a small portion of the tracts so I would need to have a command that I can modify to specify this and to specify that the index labels are based on the JHU tracts. And of course I would be able to use the same line of script but change it for MD.
the JHU files are available in a file path that is /usr/local/fsl5/data/atlases/JHU
and I have the data for each participant in my directory and with a FA and MD scan for each participant. They each also have an image where the JHU tracts have been registered into dti space.
Hopefully someone is able to help it would be greatly appreciated!
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Hi,
before you can use fslstats for extracting the FA or MD values you have to create a single ROI of the genu. fslstats is only using (binary) mask images, so fslmaths with the -thr and -uthr parameters in combination with -bin would be your friend here. The thresholds you would use normally equal to the index, but if I remember correctly the indices are shifted by 1, because black (or zero or background) isn't defined in the JHU atlas description, so the real threshold would be either 2 or 4.
So, extract the ROI from the JHU atlas and use it with fslstats.
Best,
Martin
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I am analyzing DTI images (DICOM files) of mice to compare myelination in the corpus callosum between wild type and heterozygous ANKS1B KO models. I have tried visualization via different software however I am having trouble distinguishing from noise and "true" white matter tracts. Attached image shows a whole brain example from diffusion toolkit (Trackvis) which has tracts running wild throughout the entirety of the brain which makes no anatomical sense and does not align with the literature. I am wondering what is the best way to eliminate the nonsense tracts and optimize the real or at least presumably real ones via angle/mask thresholds, btables, angle thresholds, etc. I am very new at this so any help would be appreciated.
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I suggest that you first denoise your data: this is very important. Easy to do with MRtrix3 with the 'dwidenoise' command.
Then get rid of field inhomogeneities with the MRtrix 'dwibiascorrect' command.
Then get rid of Gibbs ringing with the MRtrix 'mrdegibbs' command.
Then you can correct for eddy and movement artefacts with FSL's 'eddy' module.
Then, you will be ready to conduct analyses such as tractography.
After whole brain tractography, then you can filter the tracts to get more of the 'true' tracts using the MRtrix 'sift' module.
-Jerome
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I am interested in using this DTI atlas ( http://www.bmap.ucla.edu/portfolio/atlases/ICBM_DTI-81_Atlas/ ) but came across a publication suggesting it is not oriented properly ( https://www.frontiersin.org/articles/10.3389/fnins.2013.00004/full ). however, this was 8 years ago. Have these issues been fixed?
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Current research challenges in breast imaging using deep learning or ensemble algorithm, Tesoro Flow, Diffusion Tensor Imaging
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There is a huge gap between the paper and landing of AI on medical. The fact is that some paper just publish toy model with off-the-shelf tool (all kinds of CNN model and opencv) to run the accuracy and metrics without understanding of the details. These models are hard to extend to real scenarios of images with local complex ROI. Resort to some top conference like CVPR and MICCAI as well as some top journals in medical diagnosis. There is also standard open dataset online with order of 10GB and 100GB. If your model can handle that, then your research make sense.
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The best approach to hierarchical scalability of deep learning to maximize prediction accuracy (1)in the absence of large data, (2) and in the presence of large data.
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Building on Shafagat Mahmudova answer, shallow neural networks have at most 3 layers (input, hidden layer, and output layer). For complex problems, the hidden layer was "fat", having many neurons. This was because more layers were not computationally feasible. Deep neural networks appeared when computers became more powerful.They added more layers instead of making the one hidden layer fatter.
It is important to note that there is not one size fit all approach with DNN to maximize prediction accuracy. By trial and error, or by the use of optimization techniques (like genetic algorithms (GA)), you can identify the correct meta-parameters of your DNN model.
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Dear experts,
Hello, this is Jean who is new to use ADNI data + DTI data.
I downloaded Axial DTI data of AD & CN, and I drop those data in dcm2niigui, but it does not work to covert to 4d nil image.
So I checked the data and it has 2,714 of dcm, which is different from what I got in PPMI DTI files.
Is it a matter of axial dti files? or is there any other issue regarding this??
Thank you!
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Hi. Are you using the Windows version of dcm2niigui? If yes, then this may be your issue.
I suggest you use the Linux (or Mac) version of dcm2nii, or even better is dcm2niix. If you still have problems, then I suggest you use MRtrix3 (specifically the command called mrconvert).
How many dicoms are in the PPMI DTI files? If you know how many volumes there are supposed to be (number of diffusion directions plus number of bzero volumes), and multiply that by the number of slices per volume, then that will equal the total number of slices (dicoms).
Jerome
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Dear all,
After checking raw Diffusion-Tensor Imaging (DTI) data from some pilots, I noticed how systematically the first two directions show a b = 2150 in place of 2200.
Has it ever happened to any of you? If yes, do you have advice on how to solve it?
Thank you very much!
Edoardo
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Is DTI definitely the model you are trying to fit to the diffusion images? I ask because typically DTI is fit to b-values closer to 1000. Many models/methods will be able to handle a difference of 50 in bvals without much extra consideration.
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In cases that mild TBI without no abnormalities in conventional MRI or CT, is there any methods evaluating brain damage in the real clinical field, other than diffuse tensor tractography or diffuse tensor imaging?
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Have you tried with DWI sequence.
If the damage is in Gray matter, then you can go for FDG-PET.
Thanks
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I have a research about DTI correlations of cognitive impairment in essential tremor. We have 40 patients, 20 brain regions (right and left, total 40). 8 cognitive domains. I try to adress brain microstructural correlates of cognitive impairment in essential tremor. But I could not figure out which statistical analysis should I use from SPSS?
Help!!!
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I did partial correlation analysis controlling for age, gender, and education level. And git some results. For example we found attention is correlates with anterior thalamic radiation. But do I need to do anything else after that? Do I need correction because I did manu correlation or regression analysis?
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DTI images of brain tumors with ground truth. It is noted that there is no DTI available in the BRATS dataset.
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Dear Guevara,
Thank you for your reply, unfortunately there is no MRI multi modal images with DTI with tumors ground truth in NITRC Image Repository .
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Dear all,
I've access to a dataset of Schizophrenia patients' Diffusion MRIs in which 3 imaging sites with different acquisition protocols are included (b-values=700,800,1000, directions=64,12,6). Considering that I'm looking into differences of graph metrics and fractional anisotropy of several white matter tracts between healthy and patient groups, is it reasonable that I combine the results of my analysis for each sites and if so, how should I do that?
Bests,
Amir
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Hi,
could/should add different sites as (dummy-coded) covariates in any regression analysis you perform and of course include/discuss as limitations.
You could further assess whole brain similarity of FA values e.g. compared to an FA template (relative values across brain regions) or across sites (absolute voxel-wise values).
Hope this helps & good luck!
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Hopefully these datasets are collected at 1mm or better resolution and include the CT data down the neck to include the skull base. Typically this is not done without reason but ideally these would be from healthy subjects and include neurophys data (EEG, MEG, etc.) and DWI (HARDI, DSI, etc.) and, why not, MRA. Although any data, including data with metal artifacts from grid, strip or single electrodes, is fine and would be appreciated, as long as it can be shared publicly. 
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Well I'll be ... thank you sir. There is a lot to like there. Cheers, -Morgan
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I am interested in extracting brain clusters showing significant correlation between cortical thickness and local gyrification. I guess this can be done using --pvr flag in GLMFIT (FreeSurfer). But I am not sure how to set up this command and create contrast and fsgd files, followed by glmfit-sim command after considering age and gender as covariates.
I would really appreciate any help.
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Sahil,
I'm not sure if this may be helpful for you, but if you have tested that correlations on Qdec you should check your a mc-z.abs.th??sig.ocn.annot file on your "corr" folder and open it on tksurfer.
once your results are visible, you can save and register each cluster as a label on each of your subjects, and then run the mri_anatomical_stats command to have the mean volume/thickness/whatever value of your interest
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I am interested in isolating the corticobulbar tract, which does not exist in the TBSS or TRACULA atlases. So far, it seems my best bet is to use Freesurfer's dt_recon to use my custom surface labels as seeds for tractography. Has anyone used any other software that they can recommend?
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While TRACULA has some interesting and unique abilities, it is quite limited since it is confined to the tracts that have been pre-defined. This is largely related to the method in which tracts are automatically identified, which is also based on a "normal" brain; therefore, it is also not ideal for tracking in structurally abnormal brains.
You can certainly use freesurfer ROIs for seeds in many diffusion packages. It somewhat depends on what you are actually looking for in your research. Keep in mind that TBSS is very different from many other methods of tractography! 
For probablistic tractography, the FSL diffusion package is quite easy to use and allows use of freesurfer ROIs as seeds. If you are more interested in deterministic tractography, I have found DSI Studio to be great (also offers other methods of ODF modeling other than tensor, such as generalized q-sampling, that can give better results than DTI). MRTrix and DTI Studio are other popular diffusion packages.
You will need to determine which package best fits your needs and invest some time in learning the software from the available manuals/tutorials.
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Dear friends
I am using DTI to evaluate neuroplasticity resulted from a new therapy. I want to know is there any interpretation for significantly increase in FA of the GRAY MATTER? Is it true to use FA value for evaluation of neuroplasticity in GRAY MATTER or it is specially just for white matter? Is there any reference regarding this topic?
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I don't know your complete set up (and what the subjects did inbetween). But if you used the same scanner and get significant results in part of the GM I would consider the data meaningful.
Noise could be a problem, as in general the signal intensity with higher b-values in the GM is lower than in the WM, but if you have enough signal in your raw data in the GM than it is probably neglectable.
Even in the GM you have fibers with a myelin sheet (just have a look at publications regarding MS plaques in GM --> demylination). Just the percentage of myelin is much less, therefore a change in FA could be meaningful.
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I am doing the analysis of intracortical EEG data and I want to test my results by predicting my own signal based in Kuramoto models to be sure that my results are correct. I would like to use the structural connectivity data as a coupling strength between the nodes (brain regions based on an atlas), but I do not have DTI data from all this patients. I would like to know from where can I have access to public DTI data to get this parameters.
Thanks in advance!
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A good place to start might be the Human Connectome Project (HCP) which releases very high quality structural connectivity data estimated from diffusion MRI ( http://www.humanconnectome.com)
I'd recommend going through the HCP workbench tutorial (https://www.humanconnectome.org/software/connectome-workbench.html) to get a feel for the data and the various cortical/subcortical parcellation schemes that can be used. I'm not aware of support for AAL in HCP, but it should be relatively straightforward to generate a CIFTI-format dlabel image from an AAL label image using workbench command-line tools.
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I have acquired 100 volumes in resting fMRI data in my subjects. So I have to delete 2 or 3 or 5 volumes. If I will consider all the volumes, then is it any problem in analysis because I have 100 volumes only.
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Hi
I have tried in both ways. During pre-processing, I have deleted 2 volumes at one time and at 2nd time, I have taken all the volumes in the same subject. There is not a very big difference with or without deleting volumes.  
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Hi all,
Analyzing DTI data, I am looking for a way to measure thalamic and cortical shift of a corticothalamic connection. My hypothesis is that, in patients a given fiber tract reaches the thalami (or cortex) rostrally, for example, compared to controls.  However, I don’t know which toolbox I can use to do it. As far as I know, FSL doesn’t provide a good way to perform this analysis. Am I right?
Does anyone know a way to figure this?
Thanks!
Theo Marins.
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Hi Theo,
Interesting, but quite difficult question. Current Fibertracking algorithms only give you "the most probable" fiberBundle-prientation. In addition, the thalamus is a highly interconnected region, which makes it hard to isolate fibertracts and to see, whether they end in a different cortex location.
One approach would be to create ROIs in the cortex and thalamus and use SPMs StreamlineTracking with Global FiberTracking data (Reisert et al.). You can use Freiburgs DTI toolbox to extract the interconnecting fibertracks (between your ROIs). All these toolboxes are matlab based.
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Hello together,
I would like to estimate the direction information with CSD and then determine from that the two main direction. From these both I would like to build a two-tensor-method.
So my questions are: When I do CSD what kind of output files do I get exactly? Somebody knows a software who can do Multi-Tensor reconstruction?
thanks for your help!
greetings
Max Wichmann
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Hi Max,
Fixels from FODs have nothing to do with the diffusion tensor model.  The eigenvectors from a tensor are also not "main directions" of anything biophysical nor anatomical...  What you can get (per fixel) from an FOD, is the apparent fibre density (AFD); which does come with a reasonable piophysical interpretation.  It would be best to Google the latter term and do some reading first though.  If you've got any questions on how to work with MRtrix, we can help you out better over at our forums on http://community.mrtrix.org !
Cheers,
Thijs
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Hi all!
I have a question regarding the use of bvecs files during DTI preprocessing. I've noticed that the values in the bvecs  text file for each participant is slightly different from one another; but only marginally so. (Btw the bvecs files were converted from DICOM to NIFTI using MRIConvert, latest version).
My question is, when performing the DTIfit procedure in FSL, do I need to use each participants' own bvecs file; or can I use one bvecs file for all participants? I've seen several guides/tutorials regarding this, and it seems to me that some of these use one bvecs file for all participants.
All comments are highly appreciated!
Best, Lasse
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I would use the participants own bvecs, particularly if you are doing tractography.
The bvecs file has the information about which way the diffusion gradients were pointing. The calculated directions of the eigenvectors (and hence the direction of the calculated tracts) depends on the direction of the gradients relative to the patient.
If there are only minor differences in the bvecs, then there will only be minor differences in the results. But you don't know that there are ALWAYS only minor differences. So to be on the safe side I would go with the patients bvecs.
I guess it would be interesting to find out why the differences exist. It might depend on the position/ angulation of the subjects head
Although, assuming it is the same MR sequence for everyone, the calculated MD and FA should be the same, since they are not telling you about specific directions. 
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I runned a TBSS in FSL and would like to use a DTI atlas similar than the "JHU-ICBM-labels". Thanx a lot!
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Thank you for your input!
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As we know Tractography is the only non-invasively tool for determining and measuring the pathways in the brain through observations of local diffusivity. The uncertainty of tractography which arises for example from noise.
I'd like to know what is this noise with example if possible.
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Diffusion is a pretty noisy technique anyway (low signal to noise ratio).  From the acquisition standpoint you have noise coming from the pulse sequence used to collect it (typically a single-shot EPI), which can have spikes or distortions caused by sinuses and phase encoding direction.  Also the b-value itself can introduce noise, as the higher the b-value the lower the SNR for both GM and WM structures.  We try to compensate for some of these by collecting multiple averages of each direction...  but the noise is substantial, and that's just on the collection side of things.  
When it comes to actually estimating the tensors, the metric you are using (FA, ADC) can also have issues depending on a variety of factors impacting your data.  
Some useful papers: 
Soares, J. M., Marques, P., Alves, V., & Sousa, N. (2013).  A hitchhiker's guide to diffusion tensor imaging. Front Neurosci, http://dx.doi.org/10.3389/fnins.2013.00031
Jones, D. K., & Basser, P. J. (2004).  Squashing peanuts and smashing pumpkins”: How noise distorts diffusion-weighted MR data.  Magnetic Resonance in Medicine, 52(5), 979-933.  
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Hi everyone,
I'm fairly new to DKI tractography. I have used ExploreDTI for DTI tractography before and I know a few software packages that work well for DTI, but I wanted to use specific software packages for DKI tractography. I have used DKE to estimate the tensor and it works fine for that aspect, but unfortunately, the tractography module does not work well. I was wondering if anyone here has used any software packages that are specifically designed for DKI tractography (or are at least capable of doing that, especially resolving the crossing fibers issues, etc.) and they have got reasonable outputs. I'd appreciate if you can introduce me some of those packages.
Thanks a lot,
Ehsan
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DKI tractography is still work in progress, though: https://github.com/nipy/dipy/pull/685
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?
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Diffusivity of water in CSF is high (no diffusion barriers).
If both tissue and CSF are present in a voxel, this means that diffusivity will likely be increased compared to a voxel in which only tissue is present. 
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Hi all,
As far as I know, diffusion metrics like fractional anisotropy, and mean, radial, and axial diffusivity can give a direct clue about water diffusion and an indirect clue about myelination in a specific voxel, but to which extent this difference in myelination inferred from increased FA (e.g. between two groups) is related to increase in myelin layer thickness or it is related to myelination of unmyelinated fibers (i.e. increased number of oligodendrocytes) is unknown and I couldn't find any methods. I wonder whether there is a tool or method using DWI or combinational methods (e.g. DWI+MTR) to differentiate these two possible underlying mechanisms.
Bests,
Amir
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Hi! Interesting question indeed. Maybe NODDI, an advanced diffusion technique developed at UCL, can be something to look at? 
In comparison to regular DTI measures such as FA and RD, you get measures of orientation dispersion (ODI) as well as isotropic diffusion from NODDI, this might be something to look into. Otherwise, maybe some quantitative T2 relaxometry techniques such as mcDESPOT or Myelin Water Imaging (MWI) could be something to look into. 
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DTI studies show that a two week mindfulness training is accompanied by a decrease of axonal density in the ACC - as index by axial diffusivity. Which effects or symptoms are normally associated with such a reduction?
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I don't have a specific answer regarding the symptoms but I am just thinking if axonal loss is the only reasonable interpretation. "Reductions in radial diffusivity (RD) have been interpreted as improved myelin but reductions in axial diffusivity (AD) involve other mechanisms, such as axonal density." Axonal loss would cause reduction in AD but there are quite a few confounding factors in DTI that convolute the interpretation from my perspective. For instance, wouldn't increased isotropic diffusion cause the same effect? 
I would really like to see the same type of study done with more advanced DWI such as NODDI, Diffusion Basis Spectrum Imaging or similar. That would probably shed more light on the underlying changes in the tissue micro structure. 
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We are trying to perform in vivo diffusion tensor imaging of mouse brain on a Bruker 9.4T MRI.  I have tried both EPI-based and Spin Echo-based sequences, but have been having difficulty with respiratory motion artifacts.  In the few mice where we have near-perfect head immobilization, the spin echo images are very good.  However, most of them have respiratory motion artifacts that seriously degrade the tensor calculations.  For EPI, we are having a lot of EPI ghosting that I think may be related to respiratory motion.
We are currently using tape and gauze - what are your favorite tricks/apparatuses for fixing the heads very still?  (Any other suggestions for improving in vivo DTI quality also appreciated.)
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There are several procedures. One is to build a device to secure the front and hind legs and simultaneously tie the upper teeth with surgery thread. Also is necessary to added two lateral pillows  in order to reduce oscillatory and  involuntary movements
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Apparent diffusion coefficient can show water's molecules diffusion.
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 A quite late answer, if you want to just compute a mean ADC value, you could use the method in this paper: http://www.ncbi.nlm.nih.gov/pubmed/25242901
The method uses the statistics of the mean magnitude MR data to compute the ADC so it is more accurate than a simple regression-based technique.  
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We recently concluded a study on the prevalence and characteristics of maxillofacial injuries in patients with mTBi,  and analysed the possible influence  of maxillofacial (MF) trauma over specific cognitive deficits post trauma (namely executive function, memory and attention). We also looked out for WM tracts that were affected both in the acute and follow up phase [controlling for both the presence of maxfac injuries and as well as the CT imaging findings (intracranial lesion vs. none)]. The results were quite interesting and seem to challenge the conventional understanding and management of patients with mTBI.
We found that patients with maxillofacial injuries without intracranial lesion doing significantly worse over time in the domains of executive function and memory. Miscrostructurally, these patients seem to have poorer WM integrity especially involving the projection and association fibers (mainly corona radiata, cingulum, superior longitudinal fasiculus, optic radiation and genu of the corpus callosum).
Would appreciate your thoughts on the biophysics and biomechanics of maxillofacial trauma in mTBI  and how that could explain the findings.
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Also the energy used in the process of breaking such hard bones is not available to go through to harm the underlying tissue (that's what a safety helmet does or a football helmet, for example - helmet is toast so the tissues underneath are not).  It is also why you have to replace a bike helmet every 3 years or after any accident - the lining materials are no longer useful as an energy trap and the energies of a blow go right through.  But the majority of the brain parenchyma is only loosely associated with the pia mater so it is the blood vessels (which run through the arachnoid and have to deal with entrances and exits through bones) and the white matter tracts (which are ultimately attached through the brain stem to the spinal cord and which run through longer spaces within the brain, connecting grey areas) that are vulnerable to the energies of an acceleration/deceleration injury.  The exception of course is the olfactory bulb and tract which run through bone and get sheared off leaving folks unable to smell in frontal injuries and accel/decel ones all the time.  Have you thought about doing a simple olfactory screen with your patients to document that kind of injury?
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In other words: how many of subject should best have a tract in the voxel so that the voxel is included in the group mask? 
I have the following thresholds in my mind: 50, 60, 70, 80, 90, 100% but have no real experiences so far. 
Thank you for answering it!!
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Hi Marco,
de Reus published a paper on this which I have used as a guideline, he suggests 60% as a suitable guideline. See also here http://www.sciencedirect.com/science/article/pii/S1053811913000050
Good luck!
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My current data on neuropsychological performance in patients with mTBI across two time points (admission vs follow up) indicate that although the patients with the cortical injury do significantly worse in almost all domains of neurocognitve assessment during the acute phase, these patients, however, outperform the patients with "plausible" axonal injury (based on the DTI data) in the follow up phase (6 months post trauma)? What do you think is happening here?
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To keep it simple, let's say that neurons exhibit notable plasticity when still viable whereas axons and axon tracts, to much lesser extent when damaged.  Depending on the cortical areas and activities involved, there are vastly different levels of redundancy and neuroplasticity.  Often, cortical neuronal damage may be treated by activating parallel pathways (neural network approach as evidenced by Carrick, recently on a study of 200 soldiers, victims of blast injuries).  And that, dear sir, brings us to pathways....  Axonal damage means that the corridors of communication are disrupted and therefore cannot pass information from one area of the brain to another...depending on function and redundancy, naturally.  Neuroplasticity is compromised more by axonal damage, and the risk of diaschisis is superior in axonal damage due to a domino effect that direct cortical damage, especially in the higher centers, manifests less.  However, if the axonal damage is significant in the more medial and caudal pathways (cord to brainstem/cerebellum, brainstem/cerebellum to mesencephalon, mesencephalon to thalamus) the manifestations will be much more pronounced than in supratentorial axonal lesions.  That make sense, right?  There are more connections from the more primitive areas of the brain when compared to the more recently developed cortical areas.  We can liken this to damage to a tree trunk that has larger implications to the tree's functions than damage to an area of tiny branches and leaves.  Unfortunately, in mTBI, the major damage is to central structures where axonal damage has more pronounced domino-effects that dramatically effect the basal ganglia, with it's consequent motor, cognitive, and limbic manifestations.
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Traumatic brain injury, even in its mildest form, is known to result in degenerative processes including demyelination and dysmyelination of the axons over time. The shearing and tearing of the axons (primary injury) due to the acceleration and deceleration force of high velocity impact would also normally trigger off the secondary injury cascades. This includes the synaptic deregulation, cell death and axonal degeneration.  
But how quickly does these processes start (especially the demyelination of the axons) in patients with mild TBI? I am of the opinion that it will take at least a few days or weeks before such degenerative process starts. What are your thoughts?
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Dear Timothy Shea,
The "miserable minority" are those patients with mild traumatic brain injury who continue to experience prolonged cognitive deficits even after months and years, way beyond the average cutoffs (which is about 3-6 months post trauma).
You were absolutely right that one should consider preexisting comorbidities/(maybe even age) factors and as well as neurobehavioral/ neuropsychiatric condition arising from the trauma that could adversely influence the cognitive performance of these patients. Having said that, there are many studies (Ref 1-5) that has proven that even when all these factors are accounted for, and removed from the analysis, these individuals continue to exhibit deficits in their neurocognitive functions. So what then would best explain these manifesting derangements/ impairments ?
One possible explanation may lie in the structural changes. Patients with visible injuries -complicated mild TBI (radiologically visible lesions  i.e hematoma, contusion, edema, etc) and the corresponding functional and cognitive deficits are findings that are widely accepted by scientific community. How about the wider group of mild TBI patients whose radiological findings are usually negative and discharged from the ED without even being seen by neurosurgeons and without any subsequent follow-ups? Does normal CT/ MRI in the acute stage means there isn't any injury to start with with?
Many advances imaging protocols have proven otherwise. Diffusion Tensor Imaging (DTI) studies (reference to which I will give at the end of this reply) for example are able to pick up changes to the microstructure like the axons or the white matter tracts which are usually missed by the CT/ conventional MRI sequences. Could these structural anomalies/injuries then  explain the neurocognitive impairments experienced by the "miserable minority"? I guess the answer would be affirmative (see ref 5-8). With all these new evidences of microstructural abnormalities (even at the earliest hours post trauma), we can no longer reject the patient experiences as purely psychogenic in origin, ensuring we do our due diligence. 
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For my project, we will acquire HARDI DTI data of brain tumor patients and healthy controls in order to construct structural connectivity matrices (based on tractography results) for graph theory analysis.
Can anyone recommend any program, preferably script-based?
I heard of the Diffusion Toolkit/TrackVis, MRtrix, and Camino, but I don't know which one would be best for my purpose.
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MRTrix is good for HARDI data and user-friendly! 
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In terms of 1) efficiency  2) ability to perform ROI analysis for DTI parameters (Eigen values, FI, CL, et.)  3) efficient help (easy to follow) 4) free (for scientific purpose)
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There is a large number of variety of software for DTI analysis. There are some general packages and some that do better in certain aspects of DTI analysis. It also depends on the operating system you want to install and whether you are looking for special more advanced types of DTI-related techniques which are a bit different, such as HARDI, DSI, or diffusion kurtosis imaging. Or whether you are thinking of doing tractography with it or not. Most MRI manufacturers also have some basic software you can use. Here are some:
MedINRIA
DTI Studio -- One of the first packages by Susumu Mori's lab at Hopkins and widely used for ROI analysis
DSI Studio -- Includes DTI, DSI, Q-ball and fiber tracking by Isaac Tseng's lab in Taiwan
ExploreDTI -- Comprehensive and include nice motion correction method.
Camino -- comprehensive toolsbut not very easy to use
TrackVis -- cross-platform and does DSI
Here's also a nice list (nonexhaustive) of various software with the advanatages of each: http://www.diffusion-imaging.com/2013/03/list-of-software-tools-for-dti.html
I also would suggest looking at this paper: A hitchhiker's guide to diffusion tensor imaging http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3594764/pdf/fnins-07-00031.pdf which has a nice list of the software and their features
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Some of the missense mutations of BDNF have been theoretically implicated (rs6265 in particular) to influence various outcomes of brain injury.  Can someone please elaborate on how certain genetic polymorphisms of BDNF may aggravate cerebral oedema in mild TBI. If you have come across any such articles, please feel free to share.
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Thank you for your kind suggestions, Dr Altay. Will surely check the paper out.
I am currently working on how certain genetic predispositions actually affects the events that occurs in between the primary insult and the secondary trauma in brain injury. For example, rs6265 of BDNF has been implicated to negatively affect certain psychiatric conditions. However, not much work has been done in mTBI, especially with regards to its influence on microstructural changes across different time intervals. While working on the DTI results, we realised that some quarter of our patients with vasogenic edema and astrogliosis (evinced in the preliminary results) performing poorly in neurocognitive assessments across the time points in comparison to others from the same mTBI group. We would like to understand why..
(I posted the question in order to encourage people to share their opinions and engage one another in the effort of advancing the limited knowledge on the subject matter.)
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The image data can be stored as DICOM and NIFTI  as well.
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The DTFIT tool in the FDT toolbox of FSL is what we use, then feed this back into MATLAB to work with the diffusion tensor.
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I am currently working on MR diffusion model to monitor defects in human heart. Meanwhile, I am able to assume that diffusion coefficient is related to the relaxation rates (R1=1/T1 and R2=1/T2), I need experts to advise on which substance whose T1 and T2, I will have to use.
I need these features because the substance which is diffusing is expected to carry molecular signatures of the heart. Thank you.
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If I understand correctly, you want to build a gel phantom that reflects the T1/T2/ADC characteristics of the myocardium ? If so, look into the following articles:
You will have to adjust the formulas to match T1/T2/ADC values of the myocardium. You can find T1/T2 in this paper:
And depending on the purpose of your study, you should look into papers reporting ADC values for in vivo or ex vivo. They can differ quite a lot! Finally you should validate your phantom with a gold standard technique for each characteristic value (T1/T2/ADC).
Good luck on all that
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I have a 64 gradient plus 5 B0s DTI dataset and need to remove some intermediate volumes (ie. volumes number 10, 23, 35, 55). In fact I have a software that requires all the B0s to be at the beginning of the study, and a dataset where the B0s are distributed through the acquisition. How can I do this? Can I use Matlab? Could you suggest me the correct procedure? Alternatively I need to move the same volumes so that they all end up at the beginning of the volumes sequence.
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I know of FSL and ExploreDTI. The former has been proposedabove and needs a Linux environment. The latter is a toolbox for matlab or standalone .exe file for Windows. In ExploreDTI, you can go to Plugins >> "Shuffle/select 3D volumes in 4D .nii files". Then specify the volumes to retain like 1:3 5:32 (to remove volume 4 and retain all until 32nd). You need to know in advance which number is your bad volume, but the software has tools for that if I remember correctly.
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Alteration of sexual functioning in patients with traumatic brain injury is a phenomenon that has been widely studied. However, these studies are predominantly limited to self reported symptoms and hypothalamic-pituitary dysfunction (assessed through hormonal workouts mostly. Detailed imaging studies of this phenomenon are extremely scarce, if not unheard of.
What are your thoughts on possible influence of deep white matter tracts on sexual functioning in TBI, and MTBI specifically?
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YES. HOWEVER, THE QUESTION MIGHT NOT BE SO STRAIGHTFORWARD AS IT SOUNDS.  FOR EXAMPLE, IN APATHY, A FREQUENT AREA OF INVOLVMENT IN TBI/MTBI, GENERAL APATHY, IN WHICH LOSS OF SEXUAL INTEREST IS OFTEN INCLUDED IN A SYNDROME OF SYMPTOMS, THE DORSOLATERAL AND MEDIAL PREFRONTAL/FRONTAL REGIONS ARE TYPICALLY INVOLVED, AND THE ASSOCIATED CIRCUITS THROUGH DOPAMINERGIC PATHWAYS. MORE DORSOLATERAL INVOLVEMENT IS USUALLY CHARACTERIZED BY PASSIVITY, REDUCED VERBAL OUTPUT, SLOWNESS IN INITIATING RESPONSES TO QUESTIONS, AND FLATTENED AFFECT. APATHY IMPLIES MORE MEDIAL INVOLVEMENT, AND ITS SUBCOTRICAL PROJECTIONS TO THE VENTRAL STRIATUM/NUCLEUS ACCUMBENS REGIONS, GENERALLY A "MOTIVATIONAL DRIVER," AND THIS CAN OCCUR INDEPENDENTLY OF DEPRESSION, ALTHOUGH APATHY IS OFTEN MISTAKEN FOR DEPRESSION. HOWEVER, THE KEY HERE IS THE LINKAGE TO THE Nac, THE "HEART" OF THE DOPAMINERGIC REWARD SYSTEM. HOWEVER, IN MTBI, THERE CAN BE LACK OF DIRECT "CORTICAL" INVOLVMENT, BUT INSTEAD, INVOLVMENT OF THE CEREBELLUM, WHICH HAS BEEN DEMONSTRATED TO SERVE AS A REGULATOR OF DOPAMINERGIC ACTIVITY IN MEDIAL ANTERIOR CORTICAL/VENTRAL STRIATAL REGIONS, AKIN TO A PHENOMENON OF DIASCHIZIS. BUT, THE CEREBELLUM IS ALSO INVOLVED IN MANY OF THE FUNCTIONS ASSOCIATED WITH THE PHYSIOLOGICAL RESPONSES OF ORGASIM; SO, IN GENERAL, THE MEDIAL/ACC REGIONS OF ANTERIOR CORTEX, THE VENTRAL STRIATUM, AND THE CEREBELLUM ARE ALL INVOLVED IN SEXUAL ACTIVITY. BUT "SEXUAL FUNCTIONG " IS A VERY GENERAL TERM, AND UNDER THIS OVER-ARCHING CATEGORY WE HAVE ATTRACTION, INTEREST, CONSUMPTION, ETC., AN EXTREMELY WIDE-ARRAY OF FACTORS THAT CAN BEST BE UNDERSTOOD WITHIN THE CONTEXT OF LARGE SCALE BRAIN SYSTEMS, WHICH ALWAYS IMPLIES INVOLVMENT OF WHITE MATTER TRACTS. I AM ATTACHING A PAPER WHICH I THINK WILL BE EXTREMELY RELEVANT TO YOUR QUESTION, "IF" I UNDERSTAND IT PROPERLY. --LK (LARGE FONT FOR ME - NOT YOU)
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Hello!
I'm using the Quality Assessment tool from ExploreDTI based on residuals. There I get a diagram of absolute residual model errors per DW-Image, but I don't really understand what the measure of the scaling is. When do I know which DW-Images are corrupted, is there a specific threshold? 
And more generally:
Can I exclude some because of obviously visual artifacts? Is there a percentage of how many images I can exclude per person? Are there any guidelines how to do this right? 
Thanks a lot for your help in advance!
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If I remember correctly the graph of ExploreDTI shows deviations of single volumes (or slices) from the remaining set. It should be an intuitive and quick graph indicator. However, rather than relying only on graphs produced by software, I would suggest visually inspecting all the volumes. All you need to do  is  find a software that can open 4D files and loop through them quickly. MRIcron cannot do this. FSL have a couple of +/- buttons to loop through 4D frames. I used DTIstudio for visual inspection. In DTIstudio the volumes are listed in a dropdown menu on the right. Click once this ara and use up and down arrows to loop through volumes. It gives also an idea of subject motion, because the quick motion of volumes is perfect for the eye to detect motion. It doesn't take more than 30 seconds to have a sense of whats going on with the dataset. You can also see stripes or lines in single volumes, which may indicate a single corrupt slice.
My advise is to remove volumes that are entirely corrupt. Some say 2-3 volumes out of 32 is the limit; of course your DTI deteriorates with gradually more orientations removed. It is also important at this point to make sure different groups of subjects do not have a different level of image corruption. You may find out differences in FA that are in fact related to the difference in number of orientations left after cleanup (yes, the number of orientations seriously impacts FA). The degree to which this is true may vary, depending if you remove 5 orientations from a dataset with 60 or with 22 orientations. It may depend also whether those 5 are randomly distributed or fall on the  same angulation.
Hope this helps.
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Given that someone works with noisy datasets, what would you recommend as a quality measure? There are no repeated B0s to estimate sigma.
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There is a recent publication in Medical Physics titled: Signal-to-noise assessment for diffusion tensor imaging with single data set and validation using a difference image method with data from a multicenter study (by Wang ZJ, Chia JM, Ahmed S, Rollins NK; Med Phys. 2014 Sep;41(9):092302. doi: 10.1118/1.4893195).
The authors describe  a technique for noise measurements using high-pass filtered images and combine the noise properties from all available diffusion directions; the approach appears reasonable and sufficiently simple if no repeated measurements are available.
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I may be wrong, but I have understood that this is the only way to work with spherical deconvolution algorithms. Can you suggest any other solutions to obtain diffusion images obtainable with the same type of scanner that would allow working with spherical deconvolution algorithms?
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I will check if I am able to do this. 
Thank you again.
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Literatures are generally equivocal about negative correlation found in diffusion tensor imaging (DTI) of mTBI patients, especially when the inverse correlations are found at the initial admission DTI and neuropsychological testing. Some associate the negative correlation with cytotoxic edema (thus the increased FA vs poorer neurocognitive performance). How do you justify both positive correlation and negative correlation with poorer cognitive performance at admission?
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Don't know at which anatomical level you see the increase, but the splenium does not have much crossing fibers. Is actually one of the purest single orientation regions, if measured straight at the midline.
There is a measure called "mode of anisotropy", which is used as a marker of crossing fibers. Mode is not as established but may be useful to understand effects caused by crossing fibers when used to complement FA findings.
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I have some DWI (b=0 and 1000) scans and need the ADC maps, unfortunately not in all subjects the ADC map was computed at the console. I could write a python script that would do that, but I want to know if there is a downloadable software that would do the same.
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If you have a MacOS based workstation you can use OsiriX to do that:
(download the free version)
Install the app and than use plugin manager to add the free plugin "ADCmap".
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I am currently working on MEG data from patients with GPi and STN DBS. All connectivity studies including ours can find prominent theta connectivity between the temporal lobe and the basal ganglia structures. Theta is the most prominent hippocampal rhythm.
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Attached file
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I am currently working on simultaneous MEG-LFP recordings and would like to compare my results of oscillatory connectivity to those from imaging studies.
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These could be interesting for you:
Draganski B, Kherif F, Kloppel S, Cook PA, Alexander DC, Parker GJ, Deichmann R, Ashburner J, Frackowiak RS (2008) Evidence for segregated and integrative connectivity patterns in the human Basal Ganglia. The Journal of neuroscience : the official journal of the Society for Neuroscience 28:7143-7152.
Zhang D, Snyder AZ, Shimony JS, Fox MD, Raichle ME (2010) Noninvasive functional and structural connectivity mapping of the human thalamocortical system. Cereb Cortex 20:1187-1194.
Zhang D, Snyder AZ, Fox MD, Sansbury MW, Shimony JS, Raichle ME (2008) Intrinsic functional relations between human cerebral cortex and thalamus. J Neurophysiol 100:1740-1748.
Behrens TE, Johansen-Berg H, Woolrich MW, Smith SM, Wheeler-Kingshott CA, Boulby PA, Barker GJ, Sillery EL, Sheehan K, Ciccarelli O, Thompson AJ, Brady JM, Matthews PM (2003) Non-invasive mapping of connections between human thalamus and cortex using diffusion imaging. Nature neuroscience 6:750-757.
And this for cerebellum:
Buckner RL, Krienen FM, Castellanos A, Diaz JC, Yeo BT (2011) The organization of the human cerebellum estimated by intrinsic functional connectivity. Journal of neurophysiology 106:2322-2345.
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I am interested in starting a collaboration on processing cardiac DW-MRI sequences. Our research group has experience in brain DW-MRI processing.
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I'm conducting a study on three groups of subjects, 1 control group (HC) and two different degrees of the same pathology (D1 and D2). I would like to infer the differences of their diffusivity values. I'm using TBSS to do this. I've just compared them, two by two, using unpaired t-tests. Do you suggest to use an ANOVA design before? If yes, which should be the contrast matrix?
I suppose it should be a relatively easy question but I did not find a general agreement about this.
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Yes, of course.
I read on the TBSS forum that "though of course we always try to discourage people
from using ANOVA in general" and I read some instructions on the web but it seems that there is a little bit of confusion about this argument.
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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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I'm totally inexperienced in DTI analysis and would be grateful if someone would recommend useful tools for it. Thanks.
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Dear Shoji,
There is no simple answer, of course, as there're plenty of software packages and you will probably get different suggestions here.
As you already have some experience with neuroimaging data analysis, I would generally recommend using software that you are already familiar with (SPM/FSL?).
Currently, I do my DTI analysis mostly in FSL and Tracula (Freesurfer implementation), depending on whether I want to run it voxel- or ROI-wise. The first one (TBSS in FSL: http://fsl.fmrib.ox.ac.uk/fsl/fsl4.0/tbss/index), I think, is substantially easier for beginners, but if you're already familiar with Freesurfer and comfortable with shell scripting - then I would suggest Tracula.
If you like python, then you might be interested in looking at Nipype (http://nipy.sourceforge.net/nipype/index.html), which is a meta-package with a possibility to combine multiple toolboxes into one workflow. It also has some ready-to-use pipelines for DTI.
Recently, I was at the course, where we also had BrainSuite lab (http://brainsuite.loni.ucla.edu/). The software is well-documented and seemed relatively straightforward to work with as well.
Finally, I heard some positive feedback from people working with Camino (http://cmic.cs.ucl.ac.uk/camino//index.php). Haven't tried it myself so far, but I think I will, as Nipype has a pipeline, combining it with Freesurfer.
Good luck with your analysis!
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I'm currently comparing the DTI data of three groups of subjects using TBSS.
At first glance I found significant differences among them by performing unpaired t-tests.
After removing two outliers, due to their clinical condition, and using a multiple comparison correction strategy I didn’t find significant differences in two of the comparisons anymore. I therefore tried to compare them without corrections (using a standard threshold) and I found the expected differences again.
Does anyone has the same problem? Do you think it possible to publish uncorrected data if they are reasonable?
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The general rule of thumb is that if you predicted changes in particular areas before you collected the data, you are justified in reporting uncorrected results. But you must explain your a priori hypothesis. If you didn't have any idea where you would see changes appear in the brain, you must use corrected data. For TBSS presentation, a threshold of p<0.05 corrected is considered acceptable.
Tim
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I recently acquired the DTI images of a big sample of children (age range 2-12 years) affected by a certain pathology.
The principal aim of my work is to study the evolution of the white matter in this kind of disease. The principal issue is that we have no controls of the same age range because it's generally difficult to enrol healthy children to be sedated.
Given that it isn't correct to compare data acquired from 2 different scanners, does anyone have any suggestions?
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Article on "Pediatric diffusion tensor imaging: normal database and observation of the white matter maturation in early childhood" may help you to get controls for your study , if the database of this study is still maintained.
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There has been some discussion regarding which approach – ROI vs Whole brain analysis – is better to utilise. My personal opinion is to use every possible mean to confirm your findings. However, the question I would like to ask you is the following: if you have performed a whole brain analysis and you find a correlation in an area that is not theoretically one of the typical areas you would expect, when you perform ROI analysis, would you put that area in there along with the ones theoretically associated with your task? If you have seen this in a publication, would you criticize that this area was used as a ROI?
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The whole question of how to select ROIs is not terribly straightforward. I'm generally a big fan of ROI-based approaches, as I think they can be much more powerful than whole-brain analyses, but there are essentially two schools of though that are best exemplified by this pair of papers by Karl Friston et al. (http://www.ncbi.nlm.nih.gov/pubmed/16635579) and Saxe/Brett/Kanwisher (http://www.sciencedirect.com/science/article/pii/S1053811905025796)
Essentially the Kanwisherian (!) position is that the optimal way of defining fROIs is by a completely independent (i.e. separate set of data) localiser task. Friston's argument is that fROIs can be defined by using an orthogonal contrast from within the same task/experiment. Both methods have advantages and drawbacks, and both are pretty much acceptable these days.
The clear advantage of the Fristonian view is that you don't need an extra localiser task. It's also very useful in being able to decompose complex designs and view statistical interactions in a simple way. For instance, say you're interested in face processing, and have a balanced 2x2 factorial design with male vs. female faces as one factor, and happy vs. sad expressions as the other factor. You could, using appropriate contrasts, view the interaction of the two factors as a statistical map, however viewing interaction effects as SPMs is not generally that informative, as you can never be sure exactly which lower-level effect(s) is/are producing the activated voxels.
A better approach with that kind of design would be to define a simple t contrast of All faces > baseline. This would likely give you a nice fat blob in the fusiform face area. You could then use that ROI to pull out amplitude data related to each of your four conditions for each subject, average the data, and plot it in a simple histogram; it should be instantly clear what's going on in the data. Alternatively depending on your research question, it might also be preferable to use one of the main-effects of the factors (e.g. All male vs. female faces) to define the ROIs - this would likely give your more specific ROIs. These contrasts are still orthogonal to the lower-levels of your design and therefore independent; in fact, I had a long conversation with Tom Nichols about this, and he said that they're what statisticians call 'conditionally independent' but it boils down to the same thing.
An important aspect of this approach is that the statistical inference is done on the amplitude data pulled out of the ROI - not on the brain images. In a sense then, it doesn't matter how you define your ROIs - they can be defined at any (reasonable) corrected or uncorrected statistical threshold, and by any (reasonable) method. To (eventually - sorry) return to your original question then, I think selecting ROIs in 'unpredicted' locations is fine - and in fact it might even be somewhat scientifically irresponsible to ignore results from these locations. Clearly though, if you end up with 100 ROIs your results are going to be very onerous to interpret, and you'd have to do some kind of (Bonferroni-type) correction on your inference stats; this would be an argument for thresholding your ROI definitions at a 'standard' level e.g. p<0.001 for individual data, or p<0.05 (corrected) for group data.
Hope that helps! Sorry for the long-windedness, but these are difficult questions, and don't have short answers!
Cheers,
Matt.
PS. This site: http://web.mit.edu/evelina9/www/funcloc.html has a lot more good info on ROI methods - by one of Nancy Kanwisher's research group - Ev Fedorenko.
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The structural connectivity network can be derived from diffusion tensor imaging. However, we encounter a strange question. The MFC and PCC were defined according to the AAL template in MNI space and then were transformed to the individual DTI space for constructing the cingulum tracts. Unfortunately, there is nothing we got at last for about 40 subjects. Then, we dilated the ROI we made before and acquired some good results. My question is that is it necessary for us to dilate all the 90 ROIs to constructing the whole brain structural network for graph theory analysis?
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DTI, when only one tensor is used to characterize the fibre orientation in each voxel, is incapable of providing an accurate whole brain structural network for any kind of analysis. The reason for this is that most white matter voxels have crossing fibres, which cannot be fitted with a single tensor. At least 60 diffusion gradient directions, and a technique such as spherical harmonic decomposition, must be used to resolve multiple fibre directions. Use of a single tensor model generates nonsense. See: Jones DK, Knösche TR, Turner R. White matter integrity, fiber count, and other fallacies: The do's and don'ts of diffusion MRI. Neuroimage. 2012 Jul 23. pii: S1053-8119.
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Philips PAR files (ver. 4.1) contain the x,y,z components of each gradients direction. Whilst it is known that the order must be changed, and one sign must be flipped, my question is if the slice orientation (given in the header of the PAR) should also be taken into account. If true, this would imply a different set of bvecs per scan. Opinions?
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Answer
Dear Luca and Lindsay,
in my experience, you need to rotate the gradients according to the image slice orientation every time this orientation is not the identity (i.e. transverse slice aligned with the axes).
The overplus option only changes the gradient orientations to maximize the gradient strength. In both overplus and non-overplus situations, the directions in the par header are given in the "LPS" frame (Left-Anterior-Superior) and need to be re-oriented onto the diffusion weighted image frame.
I hope this helped.
Regards,
Nicolas Toussaint