Hello all,
I am using a memory encoding paradigm and I use the wfu pickatlas for roi definition but the mask seems that only covers gray matter (I use the broadmman areas 34, 35, 36 and hippocampus), and I lose too many voxels that appears active when no use roi. Exists any mask that cover all the medial temporal lobe region including white matter?
Thanks in advance
John Ochoa
Universidad de Antioquia
Hi,
Regarding the AAL template (which is most likely what you have already seen in the WFU pickatlas), I too have noticed that the AAL hippocampus definition tries to only cover grey matter. This exclusion leads to gaps and holes in the mask that I think you are referring to.
Given the anatomical variability between subjects in the hippocampus, it appears to me that the AAL mask attempts to be far too precise; moreover, it is not left-right symmetric. In the past, I have used AFNI's 'draw dataset' function to correct these problems with the AAL mask and used the resulting modified mask to define ROIs.
Alternatively, I've seen probabilistic anatomical masks that I think were used in FSL, and that may be worth checking out.
Best Regards,
G Elliott Wimmer
Dept. of Psychology
Columbia University
Dear Alexander:In your opinion, how does LPBA40 (http://www.loni.ucla.edu/~shattuck/resources/lpba40/) compare with the n30r83? LPBA40 only has 56 structures, but it was constructed from 10 more subjects. It seems both n30r83 and LPBA40 have maximum probability maps.I do not want to get into the details of comparing those two templates. As a user, I just want to choose one template to mask regions in my fMRI data. Any suggestions?Best,CarltonOn Sat, Feb 5, 2011 at 2:56 PM, Alexander Hammers <[log in to unmask]> wrote:
Dear Steffie,Brodmann areas are histological entities; you can therefore only really get to them via histology. There is a long-term initiative under way in Jlich, Germany to provide such maps via histological workup of ten brains and spatial transfer to MNI space - see http://www.fz-juelich.de/inm/inm-1/index.php?index=396 and papers by Zilles K, Amunts K, Eickhoff E et al.They are great but don't exist for all areas.The next best thing is to define your regions based on multiple brains. The good news is that for major sulci, there is, on average, actually a reasonable correspondence between histology and macroscopic landmarks (which you can see on MRI), see e.g. Figure 6 in Hammers A et al. Hum Brain Mapp 2007.One such atlas is our maximum probability map in MNI space (Hammers A, Allom R et al. Hum Brain Mapp 2003; since then expanded to include 83 regions and now based on 30 brains (n30r83; see Gousias IS et al. Neuroimage 2008 for the additional region definitions)), which you can get by agreeing to a one-page free academic licence (attached).There are many more atlases based on _single_ brains, including AAL (Tzourio-Mazoyer et al. 2002); our earlier segmentation of the same brain (Hammers et al. 2002); and digitalized versions based on the single Talairach hemisphere (minus cerebellum) (e.g. WFU Pickatlas). However, single brain / hemisphere atlases are by definition not representative and are therefore not likely to well represent your subject under study. This can be, and has been, quantified for AAL and PickAtlas versus the maximum probability map (Rodionov R et al.Magn Reson Imaging 2009).The most exhaustive quantification of the errors involved in single subject atlasing is in Heckemann RA et al. Neuroimage 2006. In terms of spatial overlap, for single atlases, you loose five Dice index points compared to the maxprobmap. You'd need to train a student for several months to improve that much, and by using a maximum probabilty map you get the same improvement for free :-).Obviously there are other more involved and more accurate forms of atlasing (see e.g. Heckemann RA et al. Neuroimage 2010 for one example and a review), but for working in MNI space with low-res techniques (fMRI, PET, SPECT, MEG, etc.) I think maxprobmaps are an excellent compromise.Hope this helps,ATB, APS: If you want the n30r83 maxprobmap, just email me off-list if the licence is acceptable to you (it essentially asks to cite our work and not use it commercially).On 4 Feb 2011, at 19:38, Michael T Rubens wrote:WFU pickatlascheers,MichaelOn Fri, Feb 4, 2011 at 10:30 AM, Steffie Tomson <[log in to unmask]> wrote:
Hi Everyone
Id like to pull BOLD data from a set of anatomical regions. For example, Id like to look at traces in the anterior cingulate or the left ITG. Does anyone know how to find a database of MNI-normalized anatomical regions like this? Ideally, somewhere to download individual masks for anatomical regions, or even Brodmanns areas?
Thanks so much,
Steffie
--
Research Associate
Gazzaley Lab
Department of Neurology
University of California, San Francisco
Thanks Alexander, Michael and Elliot for the reply, I will see the AAL atlas and the maxprobmap more deeply. I am in the beggining in fmri in the hippocampus forepilepsy patients, so, I will have time to explore more and more in working with roi analysis.Best wishes from AntioquiaOn Sun, Feb 20, 2011 at 10:35 PM, G Elliott Wimmer <[log in to unmask]> wrote:
Hi,
Regarding the AAL template (which is most likely what you have already seen in the WFU pickatlas), I too have noticed that the AAL hippocampus definition tries to only cover grey matter. This exclusion leads to gaps and holes in the mask that I think you are referring to.
Given the anatomical variability between subjects in the hippocampus, it appears to me that the AAL mask attempts to be far too precise; moreover, it is not left-right symmetric. In the past, I have used AFNI's 'draw dataset' function to correct these problems with the AAL mask and used the resulting modified mask to define ROIs.
Alternatively, I've seen probabilistic anatomical masks that I think were used in FSL, and that may be worth checking out.
Best Regards,
G Elliott Wimmer
Dept. of Psychology
Columbia University
Dear REST developers,
Sorry for the simple question but when I try to bandpass my dataset, the error occurs as follows:
>> rest_bandpass('C:\Program Files\MATLAB71\work\FunImgNormalizedSmoothed\sub1', 3, 0.08, 0.009, 'No', 'C:\Program Files\MATLAB71\work\Masks\mask001.img');
Ideal rectangular filter: "C:\Program Files\MATLAB71\work\FunImgNormalizedSmoothed\sub1"
Read 3D EPI functional images: "C:\Program Files\MATLAB71\work\FunImgNormalizedSmoothed\sub1".??? Error using ==> rest_ReadNiftiImage
Meet error while reading data. Please restart MATLAB, this problem may be solved.
Error in ==> rest_readfile at 61
[Outdata,Header]= rest_ReadNiftiImage(imageIN,volumeIndex);
Error in ==> rest_to4d at 41
[theOneTimePoint, VoxelSize, Header] = rest_readfile(theFilename);
Error in ==> rest_bandpass at 39
[AllVolume,vsize,theImgFileList, Header] =rest_to4d(ADataDir);
What's the problem? And how can I solve it? Thanks.
Tony
Hello SPM'ers Regards, Manish Dalwani Sr. PRA Dept. of Psychiatry University of Colorado |
_______________________________________________________________
Get the Free email that has everyone talking at http://www.mail2world.com
Unlimited Email Storage POP3 Calendar SMS Translator Much More!
_______________________________________________________________
Get the Free email that has everyone talking at http://www.mail2world.com
Unlimited Email Storage POP3 Calendar SMS Translator Much More!
Fra: Richard Binney <[log in to unmask]>Dato: Uge:8 22. feb 2011 12.48.38 CETEmne: acceptable high pass filtering
Dear all,
Something has recently been said to me that has made me think, and I want to get my head around this a bit more.....
At what point does high pass filtering becomes ineffective? It has not been a concern for me in the past, but recently it has been suggested to me that a rule of thumb is to set the high pass filter at a minimum of 1.5 x your longest SOA. That seems reasonable if this is equal to or less than 128secs. I say that because I beleive that this is the optimum filter setting to remove low-freq noise associated with cardiac/respiratory noise, etc. But what if you use a filter at a lower frequency (e.g., 260secs)? It has been said to me that this would be OK. But aren't you running a risk of alliasing signal of interest at frequencies lower than 0.01Hz with cardiac/respiratory noise etc? IF this is an acceptable risk with a filter of say 260secs, at what point does it becomes unacceptable and the filter becomes ineffective? Eg., Say you had an enomrous longest SOA of 800 secs, you might want to use a filter set at 1000secs (0.001Hz) just to be sure. would this filter be effectively redundant?
What other factors might speak to this? Jittered SOAs for example? Using a range of SOAs would of course spread the signal of interest across frequencies increasing sensitivity. Does this have an effect of reducing the risk of alliasing with low-freq noise or are you still losing a significant proprotion of your signal in the <0.1Hz frequencies (either due to noise or a 128 sec filter)? In what manner should this inform your high-pass filter? Should you be concerned with the longest SOA or the mean SOA (fundamental frequency) of your signal?
Thanks in advance for your help,
Richard
Hello,
I'm running 1st level analyses in spm8, but for some of my subjects, i'm getting the following error below, but i'm not sure why the model would not have been estimated. the error appears for 9 of my 18 subjects. the other 9 ran fine. Thanks!
Running 'Contrast Manager'
Changing directory to: /space/raid8/data/lieber/SWM/SWM08/analysis/swm_ppi_delay6s
Failed 'Contrast Manager'
Error using ==> spm_run_con at 37
This model has not been estimated.
In file "/space/raid/fmri/spm8/config/spm_run_con.m" (v3993), function "spm_run_con" at line 37.
The following modules did not run:
Failed: fMRI model specification
Failed: Model estimation
Failed: Contrast Manager
--
Meghan Meyer, M.A.
Graduate Student
Social Cognitive Neuroscience Lab
UCLA, Psychology
Window 32 bit limits you to 2 GB of memory by default . There are some tricks to boost that to 3GB as outlined by Matlab in this linkhttp://www.mathworks.com/help/techdoc/matlab_prog/brh72ex-49.html#brh72ex-67Here at the IRC we do all our processing on 64 bit linux computers. Have not had a "Out of memory error" in years.
On Thu, Feb 17, 2011 at 7:32 PM, Nero Evero <[log in to unmask]> wrote:
Hello All,
We are trying to run a fixed effects analysis using SPM8 on a Windows 7 32-bit OS with 4GM of RAM. We have 120 subjects/sessions with a total of 6600 scans (55/session). The analysis stops during the parameter estimation and gives the following erroe message:
Initialising parameters : ...done
Output images : ...initialised
Plane 1/46 , block 1/3 : ...estimation??? Out of memory. Type HELP MEMORY for your options.
Error in ==> spm_spm at 715
CY = CY + Y*Y';
Error in ==> spm_getSPM at 233
SPM = spm_spm(SPM);
Error in ==> spm_results_ui at 277
[SPM,xSPM] = spm_getSPM;
??? Error while evaluating uicontrol Callback
I tried adding more memory to SPM (i.e. by changing defaults.stats.maxmem to about 2GB), but I still received the error. Looking through the archives it seems like the common solution is to change to a 64-bit OS, but I wanted to know if there was any other possible solutions? Thanks in adavance.
Nero
Hello,
I had a look at the values in DCM.A after I have estimated the DCMs.
What does a negative value in the intrinsic connection mean? How do I interprete this result?
Thanks for your help.
BW, Maria
It is a bit suspicious if all the u_*.nii are just zero. I'm not sure
why this would be the case. Do the template*.nii files seem to be OK?
Best regards,
John
On 24 February 2011 15:03, Simon Vandekar <[log in to unmask]> wrote:
> Hi John and SPMers,
>
> I am attempting to normalise my fMRI data using the steps described in pages 440 and 441 of the spm8 manual:
>
> 1. Slice timing
> 2. Realign: Estimate and Reslice
> 3. Coregister the structural to functional images
> 4. Registration looks good.
> 5. Segment the anatomicals the SPM5 routine
> 6. Initial Import- use the *seg_sn files
> 7. Run Dartel with dependency on the gray and white matter images output by import step
> 8. Normalise to MNI, select the template6 from dartel output, the subject's flow field, and normalise the ra* files (slice timing, resliced).
>
> The output I get is sdra* files. When I compare the output to the mni template it looks as if the subject's image has not been normalised. I am suspicious of the flow field because if I look at the individual subjects' uc1* image it is an empty black box.
>
> What am I doing incorrectly here?
>
> Thanks in advance,
> Simon
>
Hello,
I'm running 1st level analyses in spm8, but for some of my subjects, i'm getting the following error below, but i'm not sure why the model would not have been estimated. the error appears for 9 of my 18 subjects. the other 9 ran fine. Thanks!
Running 'Contrast Manager'
Changing directory to: /space/raid8/data/lieber/SWM/SWM08/analysis/swm_ppi_delay6s
Failed 'Contrast Manager'
Error using ==> spm_run_con at 37
This model has not been estimated.
In file "/space/raid/fmri/spm8/config/spm_run_con.m" (v3993), function "spm_run_con" at line 37.
The following modules did not run:
Failed: fMRI model specification
Failed: Model estimation
Failed: Contrast Manager
--
Meghan Meyer, M.A.
Graduate Student
Social Cognitive Neuroscience Lab
UCLA, Psychology
The flow field looks pretty OK to me. I thought that you were
referring to something that was uniformly grey. This is the x
component. The other components can be viewed by looking at
u_rc1*_Template.nii,2 and u_rc1*_Template.nii,3 (by changing the 1 in
the file selector to 1:3).
Also note that the main objective of Dartel is to align all the scans
in the study together. When you normalise to MNI space, the images
that are closely aligned within study are all affine transformed to
MNI space. The version of MNI space is only really defined for affine
aligned brain images (ie the rather blurred averages). You should
note that the single subject brain that people often overlay their
results on does not define MNI space. It is just the scan of a single
individual that has been affine registered to MNI space.
Best regards,
-John
On 24 February 2011 16:27, simon vandekar <[log in to unmask]> wrote:
> Thanks for the reply John,
>
> As far as I can tell the template*.nii files look good, they are gray matter
> images. There is also a u_rc1*_Template.nii in the same folder as my
> template*.nii files, I've attached the image for that. Is that what my flow
> fields are suppose to look like?
>
> Thank you,
> Simon
>
> On Thu, Feb 24, 2011 at 10:47 AM, John Ashburner <[log in to unmask]>
> wrote:
>>
>> It is a bit suspicious if all the u_*.nii are just zero. I'm not sure
>> why this would be the case. Do the template*.nii files seem to be OK?
>>
>> Best regards,
>> John
>>
>> On 24 February 2011 15:03, Simon Vandekar <[log in to unmask]> wrote:
>> > Hi John and SPMers,
>> >
>> > I am attempting to normalise my fMRI data using the steps described in
>> > pages 440 and 441 of the spm8 manual:
>> >
>> > 1. Slice timing
>> > 2. Realign: Estimate and Reslice
>> > 3. Coregister the structural to functional images
>> > 4. Registration looks good.
>> > 5. Segment the anatomicals the SPM5 routine
>> > 6. Initial Import- use the *seg_sn files
>> > 7. Run Dartel with dependency on the gray and white matter images output
>> > by import step
>> > 8. Normalise to MNI, select the template6 from dartel output, the
>> > subject's flow field, and normalise the ra* files (slice timing, resliced).
>> >
>> > The output I get is sdra* files. When I compare the output to the mni
>> > template it looks as if the subject's image has not been normalised. I am
>> > suspicious of the flow field because if I look at the individual subjects'
>> > uc1* image it is an empty black box.
>> >
>> > What am I doing incorrectly here?
>> >
>> > Thanks in advance,
>> > Simon
>> >
>
>
Hi,
I stuides patients with stroke doing some behavior paradigm in fMRI.
Patients always used their affected limb for the task so some patients used left hand some used right hand.
Now I would like to flip the left and right of the images so patients' lesional hemisphere are on the same side.
Is there any script in spm 5 or 8 can help to accomplish this goal?
I found a m file called spm_flip.m but it seems support only spm2.
Your assistance are well appreciated.
Thank you so much.
Janice
You should use the flexible factorial for repeated measure designs
with factors for subject and condition in your design.
Best Regards, Donald McLaren
=================
D.G. McLaren, Ph.D.
Postdoctoral Research Fellow, GRECC, Bedford VA
Research Fellow, Department of Neurology, Massachusetts General
Hospital and Harvard Medical School
Office: (773) 406-2464
=====================
This e-mail contains CONFIDENTIAL INFORMATION which may contain
PROTECTED HEALTHCARE INFORMATION and may also be LEGALLY PRIVILEGED
and which is intended only for the use of the individual or entity
named above. If the reader of the e-mail is not the intended recipient
or the employee or agent responsible for delivering it to the intended
recipient, you are hereby notified that you are in possession of
confidential and privileged information. Any unauthorized use,
disclosure, copying or the taking of any action in reliance on the
contents of this information is strictly prohibited and may be
unlawful. If you have received this e-mail unintentionally, please
immediately notify the sender via telephone at (773) 406-2464 or
email.
On Thu, Feb 24, 2011 at 6:28 PM, Pilar Archila-Suerte
<[log in to unmask]> wrote:
> Dear SPM list,
> In setting ANOVAs in SPM, do the subject files need to be in the same order
> under each cell?
> I just ran two trial trial batches, one with the same order of subjects and
> one with a different order. The areas of activity are very similar but the
> order in which the areas show up as more or less intensive did vary.
> Any insight as to how SPM does this? should I stick to the same order of
> subjects for clarity?
> Thanks,
> Pilar A .
_______________________________________________________________
Get the Free email that has everyone talking at http://www.mail2world.com
Unlimited Email Storage POP3 Calendar SMS Translator Much More!
_______________________________________________________________
Get the Free email that has everyone talking at http://www.mail2world.com
Unlimited Email Storage POP3 Calendar SMS Translator Much More!
PhD position in Decision Neuroscience at the Erasmus University Rotterdam
and Donders Institute Nijmegen
The Erasmus Center for Neuroeconomics (ECNE) at the Rotterdam School of Management, Erasmus University and the Donders Institute for Brain, Cognition and Behaviour at Radboud University Nijmegen announce an opening for a PhD researcher to join an active group of researchers in the Center in the field of decision neuroscience/neuroeconomics/neuromarketing. This position will be jointly supervised by Prof. Ale Smidts (Erasmus), Dr. Alan Sanfey (Donders Institute) and Dr. Maarten Boksem (Erasmus and Donders).
We currently seek outstanding applicants whose research interests lie at the intersection of decision-making, behavioral economics, and neuroscience and who are interested in studying the brain mechanisms that underlie decision-making. Particular interests of our group are the neural mechanisms that underlie social influences on decisions, emotion regulation and self-control involved in decision-making, and the role of risk and reward in such decisions. We are especially interested in applicants whose research can build bridges between existing strengths in consumer-behavior research at the marketing department of the Rotterdam School of Management and decision neuroscience at the Donders Institute.
The Erasmus Center for Neuroeconomics is dedicated to conducting cutting-edge interdisciplinary research in decision neuroscience, and hosts the Erasmus Behavioral Lab which provides an excellent infrastructure for conducting behavioral and EEG/ERP experiments. The Donders Institute is a leading research institute in cognitive neuroscience and provides excellent resources for functional neuroimaging by means of two research-dedicated fMRI scanners, an MEG scanner, and EEG and TMS facilities. Additional facilities are available for the collection and analysis of genetic samples. The collaboration between Erasmus University and the Donders Institute provides an outstanding environment for studying the neural underpinnings of decision-making behavior, and the successful applicant will have full access to the facilities in both institutions.
Requirements for the PhD position
Successful candidates must have a relevant Masters degree, preferably with a background in cognitive neuroscience, cognitive psychology, or biological psychology. Candidates with a background in consumer behavior or economics, with proven evidence or a strong interest in developing cognitive neuroscience and imaging skills, are also invited to apply. A tailored PhD course program will be developed. The PhD position is for four years. PhDs receive a regular employment contract for a doctoral student. See the ERIM Doctoral Program for further information on the facilities of PhD students at Erasmus.
Preferred starting date: September 2011.
Applications, including CV, a brief summary of current and proposed research, and at least two letters of recommendation, should be submitted through the ERIM application website (http://www.erim.eur.nl). submit your application preferably before April 1, 2011. PhD applicants may be requested to provide GRE/ GMAT scores and TOEFLl /IELTS language scores. For further information on the position, visit our website erim.nl/neuroconomics or contact Prof. Ale Smidts ([log in to unmask]) or Dr. Maarten Boksem ([log in to unmask]).
how about this:%%%v = spm_vol('/image_path/brain.img');x = spm_read_vols(v);x = x(size(x,1):-1:1,:,:);[path file] = fileparts(v.fname);v.fname = fullfile(path,['flipped_' file');spm_write_vol(v,x)%%%cheers,michaelOn Thu, Feb 24, 2011 at 9:35 AM, Chien-Ho Lin <[log in to unmask]> wrote:Hi,
I stuides patients with stroke doing some behavior paradigm in fMRI.
Patients always used their affected limb for the task so some patients used left hand some used right hand.
Now I would like to flip the left and right of the images so patients' lesional hemisphere are on the same side.
Is there any script in spm 5 or 8 can help to accomplish this goal?
I found a m file called spm_flip.m but it seems support only spm2.
Your assistance are well appreciated.
Thank you so much.
Janice
--
Research Associate
Gazzaley Lab
Department of Neurology
University of California, San Francisco
[log in to unmask]" type="cite">I'm not sure, but I think you can use the image calculator in SPM, with the following function:
f = 'Inv(i1)'
On 25/02/2011, at 10:16 AM, Michael T Rubens wrote:
how about this:%%%
v = spm_vol('/image_path/brain.img');x = spm_read_vols(v);
x = x(size(x,1):-1:1,:,:);
[path file] = fileparts(v.fname);v.fname = fullfile(path,['flipped_' file');
spm_write_vol(v,x)
%%%
cheers,michael
On Thu, Feb 24, 2011 at 9:35 AM, Chien-Ho Lin <[log in to unmask]> wrote:
Hi,
I stuides patients with stroke doing some behavior paradigm in fMRI.
Patients always used their affected limb for the task so some patients used left hand some used right hand.
Now I would like to flip the left and right of the images so patients' lesional hemisphere are on the same side.
Is there any script in spm 5 or 8 can help to accomplish this goal?
I found a m file called spm_flip.m but it seems support only spm2.
Your assistance are well appreciated.
Thank you so much.
Janice
--
Research Associate
Gazzaley Lab
Department of Neurology
University of California, San Francisco
-- Rosa Maria Sanchez Panchuelo Post-doctoral Research fellow Sir Peter Mansfield Magnetic Resonance Centre University of Nottingham University Park Nottingham, NG7 2RD United Kingdom +44 115 84 66003
This message and any attachment are intended solely for the addressee and may contain confidential information. If you have received this message in error, please send it back to me, and immediately delete it. Please do not use, copy or disclose the information contained in this message or in any attachment. Any views or opinions expressed by the author of this email do not necessarily reflect the views of the University of Nottingham.
This message has been checked for viruses but the contents of an attachment may still contain software viruses which could damage your computer system: you are advised to perform your own checks. Email communications with the University of Nottingham may be monitored as permitted by UK legislation.
--------------040105050906060404020507-- ========================================================================Date: Fri, 25 Feb 2011 08:19:45 -0600 Reply-To: Michael Harms <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: Michael Harms <[log in to unmask]> Subject: Re: script to flip MR images left to right (or vice versa) Comments: To: Rosa Sanchez Panchuelo <[log in to unmask]> In-Reply-To: <[log in to unmask]> Content-Type: text/plain Mime-Version: 1.0 Content-Transfer-Encoding: 7bit Message-ID: <[log in to unmask]> A note of caution about using fslswapdim in this manner however: If you do that, the data order and the header information stored in the sform/qform will no longer be in sync. So, what the sform/qform defines to be "left" will no longer be the subject's true left (assuming the labels in the original data were correct)! Read the following FSL page if you want all the details: http://www.fmrib.ox.ac.uk/fsl/avwutils/index.html Best, -MH On Fri, 2011-02-25 at 11:54 +0000, Rosa Sanchez Panchuelo wrote: > You can do it easily with fsl: > fslswapdim input_image.img -x outpu_image.img > -x will flip the image in the RL direction. > > On 25/02/2011 11:39, Richard Morris wrote: > > I'm not sure, but I think you can use the image calculator in SPM, > > with the following function: > > > > > > f = 'Inv(i1)' > > > > > > > > On 25/02/2011, at 10:16 AM, Michael T Rubens wrote: > > > > > how about this: > > > %%% > > > > > > > > > v = spm_vol('/image_path/brain.img'); > > > x = spm_read_vols(v); > > > > > > > > > x = x(size(x,1):-1:1,:,:); > > > > > > > > > [path file] = fileparts(v.fname); > > > v.fname = fullfile(path,['flipped_' file'); > > > > > > > > > spm_write_vol(v,x) > > > > > > > > > %%% > > > > > > > > > > > > > > > cheers, > > > michael > > > > > > On Thu, Feb 24, 2011 at 9:35 AM, Chien-Ho Lin > > > <[log in to unmask]> wrote: > > > Hi, > > > > > > I stuides patients with stroke doing some behavior > > > paradigm in fMRI. > > > Patients always used their affected limb for the task so > > > some patients used left hand some used right hand. > > > Now I would like to flip the left and right of the images > > > so patients' lesional hemisphere are on the same side. > > > > > > Is there any script in spm 5 or 8 can help to accomplish > > > this goal? > > > > > > I found a m file called spm_flip.m but it seems support > > > only spm2. > > > > > > Your assistance are well appreciated. > > > > > > Thank you so much. > > > > > > Janice > > > > > > > > > > > > -- > > > Research Associate > > > Gazzaley Lab > > > Department of Neurology > > > University of California, San Francisco > > > > > > > > > > -- > Rosa Maria Sanchez Panchuelo > Post-doctoral Research fellow > Sir Peter Mansfield Magnetic Resonance Centre > University of Nottingham > University Park > Nottingham, NG7 2RD > United Kingdom > +44 115 84 66003 > > > > > > This message and any attachment are intended solely for the addressee > and may contain confidential information. If you have received this > message in error, please send it back to me, and immediately delete > it. Please do not use, copy or disclose the information contained in > this message or in any attachment. Any views or opinions expressed by > the author of this email do not necessarily reflect the views of the > University of Nottingham. > > This message has been checked for viruses but the contents of an > attachment may still contain software viruses which could damage your > computer system: you are advised to perform your own checks. Email > communications with the University of Nottingham may be monitored as > permitted by UK legislation. > ========================================================================Date: Fri, 25 Feb 2011 15:23:11 +0100 Reply-To: =?ISO-8859-1?Q?Rainer_Bgle?= <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: =?ISO-8859-1?Q?Rainer_Bgle?= <[log in to unmask]> Subject: Re: Negative values in DCM.A Comments: To: Maria Dauvermann <[log in to unmask]> In-Reply-To: <[log in to unmask]> MIME-Version: 1.0 Content-Type: multipart/alternative; boundaryMessage-ID: <[log in to unmask]> --00504502e3d0ea2d57049d1c1166 Content-Type: text/plain; charset=ISO-8859-1 Content-Transfer-Encoding: quoted-printable Dear Maria, to clarify a little: -self connections need to be negative or the activation in this region would go to infinity (and with that the whole network would become unstable) -all connections are (defined) relative to self-connections because the self-connections are 'fixed' to mean=-1 with priors for their mean and variance such that the probability of the system becoming unstable being very small. (This leaves a renormalization parameters corresponding to a decay rate, fixed to 1s.) -connections in DCM are change rates, i.e. a12 tells you how much of the current state/activation (on neuronal level) at time t of region 2 is added to the change in region 1 (at time t). There might be influences from other regions on region 2 which have to be considered, but if if a12 is the sole connection, we can say: *** a12=0.1 means 10% of the state in region 2 is added to the state (activity) of region 1 and analog for a12=-0.1, this corresponds to a subtraction by 10% of the state in region 2 from the state in region 1. Regards, Rainer On Fri, Feb 25, 2011 at 8:48 AM, Maria Dauvermann < [log in to unmask]> wrote: > Dear Rainer, > > thank you very much for your reply. Now I understand why there are negative > values in the self connections but I also have positive values in some DCMs. > What does that mean? > > BW, Maria > > > > > On 24 February 2011 16:14, Rainer Bgle <[log in to unmask]>wrote: > >> Hello Maria, >> >> as far as I understand this, connection parameters in DCM are change >> rates, i.e. area 1 reduces the activity in area 2 (if a21 < 0). >> >> As stated in Friston 2003, connection parameters are always relative to >> parameters of self connections (which have to be negative to ensure a stable >> system). >> >> Regards, >> Rainer >> >> >> >> On Thu, Feb 24, 2011 at 8:58 AM, Maria Dauvermann < >> [log in to unmask]> wrote: >> >>> Hello, >>> >>> I had a look at the values in DCM.A after I have estimated the DCMs. >>> >>> What does a negative value in the intrinsic connection mean? How do I >>> interprete this result? >>> >>> Thanks for your help. >>> >>> BW, Maria >>> >> >> > --00504502e3d0ea2d57049d1c1166 Content-Type: text/html; charset=ISO-8859-1 Content-Transfer-Encoding: quoted-printable Dear Maria,Dear Rainer,
thank you very much for your reply. Now I understand why there are negative values in the self connections but I also have positive values in some DCMs. What does that mean?
BW, Maria
On 24 February 2011 16:14, Rainer Bgle <[log in to unmask]> wrote:Hello Maria,
as far as I understand this, connection parameters in DCM are change rates, i.e. area 1 reduces the activity in area 2 (if a21 < 0).
As stated in Friston 2003, connection parameters are always relative to parameters of self connections (which have to be negative to ensure a stable system).
Regards,
Rainer
On Thu, Feb 24, 2011 at 8:58 AM, Maria Dauvermann <[log in to unmask]> wrote:Hello,
I had a look at the values in DCM.A after I have estimated the DCMs.
What does a negative value in the intrinsic connection mean? How do I interprete this result?
Thanks for your help.
BW, Maria
Hi Stephen,
Are you using DCM8 or DCM10?
If your connection R1==>R2 is not significant at the group level, then it is possible that (1) your modulatory parameter MI1 is significant, which may suggest that the inter-regional interaction between R1 and R2 increased "specifically" during your context MI1; or (2) you have a large inter-subject variability on this connection. For a similar situation, see Figure 24 in Karl's 2003 paper (Friston 2003 p1298: connection V1-to-V5 not significant but motion modulation was significant)...
I hope this helps,
Mohamed
On 25/02/2011 16:23, Stephen J. Fromm wrote:
I'm working on a DCM project. So far the best fitting model looks like this:
DI--->R1 ==> R2 ==> R3
^ ^
| |
MI1 MI2
That is:
* there are three regions, with intrinsic connections from R1 to R2 and R2 to R3
* there is one driving input DI acting on R1
* there are two modulatory connections MI1 and MI2 acting on the two between-regions intrinsic connections
(The experimental variables for MI1 and MI2 are identical.)
At the group level, the second connection (R2 ==> R3) is significant, but the first (R1 ==> R2) isn't. Conceptually, this doesn't make sense, insofar as the only way the system perturbations introduced by the DI can get to (R2 ==> R3) is through (R1 ==> R2).
Does this reduce the credibility of the model? Or is it alone not enough to do that because of the possible vagaries of what is significant?
TIA,
S
[log in to unmask]" type="cite">Mohamed
Would your response below differ depending on whether he was using DCM8 vs. DCM10?
Darren
On Fri, Feb 25, 2011 at 10:44 AM, Mohamed Seghier <[log in to unmask]> wrote:
Hi Stephen,
Are you using DCM8 or DCM10?
If your connection R1==>R2 is not significant at the group level, then it is possible that (1) your modulatory parameter MI1 is significant, which may suggest that the inter-regional interaction between R1 and R2 increased "specifically" during your context MI1; or (2) you have a large inter-subject variability on this connection. For a similar situation, see Figure 24 in Karl's 2003 paper (Friston 2003 p1298: connection V1-to-V5 not significant but motion modulation was significant)...
I hope this helps,
Mohamed
On 25/02/2011 16:23, Stephen J. Fromm wrote:
I'm working on a DCM project. So far the best fitting model looks like this:
DI--->R1 ==> R2 ==> R3
^ ^
| |
MI1 MI2
That is:
* there are three regions, with intrinsic connections from R1 to R2 and R2 to R3
* there is one driving input DI acting on R1
* there are two modulatory connections MI1 and MI2 acting on the two between-regions intrinsic connections
(The experimental variables for MI1 and MI2 are identical.)
At the group level, the second connection (R2 ==> R3) is significant, but the first (R1 ==> R2) isn't. Conceptually, this doesn't make sense, insofar as the only way the system perturbations introduced by the DI can get to (R2 ==> R3) is through (R1 ==> R2).
Does this reduce the credibility of the model? Or is it alone not enough to do that because of the possible vagaries of what is significant?
TIA,
S
--
Darren Gitelman, MD
Northwestern University
710 N. Lake Shore Dr., 1122
Chicago, IL 60611
Ph: (312) 908-8614
Fax: (312) 908-5073
I am working with some data by SPM8,as a question how can I display my brain glass result on multiple slices of atemplate MRI to view regions of hyperperfusion and hypoperfusion?
When you explore your results, in the window "Results" click on "overlays" in the group "Display". Here you can select "sections". In the menu wich is opened by this action you can select either a normalized individual structural image form the proper folder of your subject or a T1 image from SPM8\canonical. I hope this is helpful. Vladimir --- On Sun, 2/27/11, Faezeh Vedaei <[log in to unmask]> wrote:
|
Hello all,
I have a particular group level analysis for which I want to use spm. However, till now I don't manage to so I hope you have suggestions for me.
I use fMRI for the prediction of a certain binary outcome. Therefore, I use a different classification software package that computes prediction-accuracies for all voxels. For each subject, I have a .nii file with as values the accuracies (range between 0 and 1). So, this is a brain map just like you usually feed to the second level.
What I want to test in the second level analysis is which brain areas have accuracies significantly higher than 0.5. So I want to test against the null-hypothesis that accuracies are 0.5 or lower. Now the problem is that the 1 sample t-test in spm on default tests against the null-hypothesis that the values are zero or higher.... So my first question is whether it is possible to set a custum value for the t-test to test against a manually set value?
I already tried a different approach, namely subtracting 0.5 from the values in my input images (to put them in the default spm t-test). However, when I trie to do this with the image calculator of spm (with as expression i1 - 0.5) I do not get correct results: I do not get a map with exactly 0.5 subtracted from each value but a range between 0.4-0.6. It seems that spm-image-calc is also doing something else with the image... So my second question is: how can I make image-calc subtract exactly 0.5 from all values in the image?
Thanks in advance!
Best regards,
Nynke van der Laan
Applications are invited for an 18
month postdoctoral
fellowship in
the human multimodal neuroimaging project “Linking the major system
markers for typical and atypical brain development: a multimodal imaging and
spectroscopy study” (http://www.zihp.uzh.ch/1610.php#45)
funded by the Zrich Institute of Human Physiology.
This study will investigate the
major physiological markers of brain development, using a combination of multimodal
brain imaging (e.g., simultaneous EEG-fMRI, see Lchinger et al., 2011, NeuroImage,
in press)
and MR-spectroscopy methods (i.e., GABA, see O’Gorman et al., 2011, J. of
Mag. Reson. Img., in press). The initial phase of the study will establish
baseline neurotransmitter levels, cerebral blood flow (e.g., perfusion MRI) and
EEG frequency and power at rest in children, adolescents, and adults. Examining
the interactions between these markers and the changes they demonstrate with
age and hormone levels will allow to better understanding the global and
regional processes underlying brain maturation. In addition, we will
investigate changes in these physiological markers with (a) memory tasks (see
Michels et al., 2010, PLoS ONE) and (b) attention deficit hyperactivity disorder
(ADHD, see Doehnert et al., Biol Psychiatry, 2010). The starting
date of the position is May 2011.
Our department is equipped with 64-channel fMRI-compatible EEG equipment and a
3 Tesla GE scanner, which is mainly dedicated for research questions.
The successful applicant will have a
PhD research background in Cognitive Neuroscience, Neurophysiology, Psychology,
Neuropsychology, or related fields. Fluency in English and the ability to work within a
multidisciplinary team are essential. Applicants must be experienced at
conducting fMRI and/or EEG studies –demonstrated by at least 2 first
author publications in international peer-reviewed journals– and be
familiar with analysis software such as SPM/Matlab, BrainVoyager and/or
FSL. Experience with stimulus presentation software (such as Presentation),
UNIX, and programming languages a plus.
Salaries are in accordance with the
Swiss National Research Foundation.
APPLICATION INSTRUCTIONS: To apply, please send a
curriculum vitae, a personal statement describing research interests, 3 letters
of recommendations, and up to 3 article reprints/preprints (max. 2 MB!!!) to:
Dr Lars Michels
MR-Zentrum
University Children’s Hospital
Steinwiesstrasse 75
Zrich 8032
Switzerland
Reviews of applications will begin
on the 1st of March and
will continue until the position is filled.
Dear SPM experts,
If you adjust for intra cranial volume in a dartel analyses on gray matter, would it be best to have your ICV calculated from the RC files or the C files. I have read that C files are a little bit more accurate than the RC files. Then again, the dartel analysis uses RC files, therefore volumes calculated from RC files would match the data best, right? Kind regards,
Vincent
Hi Darren G/ Karl F/other PPI-ers,I can't find any posts on this and wondered if you can clear it up for me.In extracting the principal eigenvariate from your VOI, you are asked if you want to adjust the extracted timecourse. When should you adjust and when should you not worry? what is the impact of adjusting?My impression was that the raw timecourse would be extracted always. The option to adjust suggests this is not always true. Do you use this option (only?)when you have time or dispersion derivatives and/or motion regressors?I have a parametric design with two conditions (tasks)and two parametric modulations per condition. The design matrix therefore has 6 regressors of interest. Motion regressors are also included. In extracting the timecourse should I adjust using an F-contrast spaning the first 6 columns only ([1 1 1 1 1 1]->right-padded with zeros)?? Is it problematic if I have not done this? What are the consequences?What about if I were only interested in the parametric modulations of the first condition in the PPI analysis? Should I adjust for the first 3 columns only ([1 1 1 0 0 0])?All your comments will be greatly appreciated.Richard
I think that was a bug in an older version of ArtRepair. Do you have the latest version?
Pilar Archila-Suerte wrote:
SPM Users,
When using the "scaling to percent signal change" option in ArtRepair, sometimes I get what I want from the files selected and sometimes I don't (depending on which files I select).
Why would ArtRepair give this answer:
/Direct calls to spm_defauts are deprecated./
/Please use spm('Defaults',modality) or spm_get_defaults instead./
/Automatically estimated peak and contrast scaling./
/ Normalizing by beta_0008.img/
/Peak value = 4.97/
/Contrast sum = 0.996/
/Mean value = NaN/
/(peak/contrast_sum)*100/bmean = /*/NaN/*
/
/
/ans =/
/
/
/ 4.9700 0.9960 /*/ NaN/*
All files follow the same path so I don't think that's the problem. Does anybody know why this is happening and how I can fix this?
Thank you,
Pilar A.
Hello.I have question relating to setting up a flexible factorial model with 2 groups, each group has4 conditions.I have 3 factors: 1 = subject (independent, variance equal), 2 = group (independent, variance not equal), 3 = condition (not independent, variance equal)For the subject level, I entered in 20 subjects, for each subject, I entered in the the 4 scans: con_image 1,2,3,4. For conditions, I entered 1 2 3 4. Once all 20 subjects were completed, I entered 3 main effects, and interaction: main effect: 1, main effect: 2, main effect: 3, interaction: 2 3.The model did not run and I got the following error message:Running job #2
-----------------------------------------------------------------------
Running 'Factorial design specification'
Failed 'Factorial design specification'
Index exceeds matrix dimensions.
In file "C:\Documents and Settings\JWest\Desktop\spm8\config\spm_run_factorial_design.m" (v3067), function "spm_run_factorial_design" at line 482.The following modules did not run:
Failed: Factorial design specificationI feel like the error is in the subject level: either the scan or the conditions. I do not feel like the conditions step is correct. Can anyone please explain what I did wrong setting up my model.Thank you for any suggestions.JefJeffrey West, M.A.
Research Analyst
Maryland Psychiatric Research Center
Baltimore, Maryland 21228-0247
Phone: 410-402-6018
email: [log in to unmask]
Hi Jeff,You should enter conditions for subjects in a nscans x factor matrix. The rows are your scans (4 in your case) and the columns indicate the factors (2, one for group and one for condition, subject is automatically modelled).So for a subject in group 2 with scans for all four conditions you would need:[2 1
2 2
2 32 4]Make sure your scans are entered in the right order!Also, main effect for subjects doesn't need to be specified, this is done automatically (this is because you chose 'Subjects' at the 'Specify Subjects or all Scans & Factors').Good luck,PieterOn Mon, Feb 28, 2011 at 3:14 PM, Jeffrey West <[log in to unmask]> wrote:
Hello.I have question relating to setting up a flexible factorial model with 2 groups, each group has4 conditions.I have 3 factors: 1 = subject (independent, variance equal), 2 = group (independent, variance not equal), 3 = condition (not independent, variance equal)For the subject level, I entered in 20 subjects, for each subject, I entered in the the 4 scans: con_image 1,2,3,4. For conditions, I entered 1 2 3 4. Once all 20 subjects were completed, I entered 3 main effects, and interaction: main effect: 1, main effect: 2, main effect: 3, interaction: 2 3.The model did not run and I got the following error message:Running job #2
-----------------------------------------------------------------------
Running 'Factorial design specification'
Failed 'Factorial design specification'
Index exceeds matrix dimensions.
In file "C:\Documents and Settings\JWest\Desktop\spm8\config\spm_run_factorial_design.m" (v3067), function "spm_run_factorial_design" at line 482.The following modules did not run:
Failed: Factorial design specificationI feel like the error is in the subject level: either the scan or the conditions. I do not feel like the conditions step is correct. Can anyone please explain what I did wrong setting up my model.Thank you for any suggestions.JefJeffrey West, M.A.
Research Analyst
Maryland Psychiatric Research Center
Baltimore, Maryland 21228-0247
Phone: 410-402-6018
email: [log in to unmask]