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Dear Li Bei, 1. Connectivities between regions are inferred on the basis of the values of the intrinsic connections (stored in DCM.A) and our uncertainty in these estimates. These define the posterior distribution ie. our belief in the values of these parameters after having fitted the data. Using the 'contrasts of connections' option on them we can then see how probable it is that a certain connection is eg. greater than 0. Your connection A(2,1) - which I think is from region 1 to region 2 - seems large. You can get the posterior probability of it being bigger than 0 as described above. Similarly for A(3,1). It may be that your strong connections are only the forward ones. Re questions 2 and 3. These are really cognitive neuroscience questions which require an expertise in the particular system you are studying. But generally, driving inputs describe stimulus bound attributes that enter sensory regions and modulatory connections are of a more psychological nature. Best, Will. Li Bei wrote: > Dear Penny: > I got some other problems in DCM. > While i got a set of brain regions with T-contrast > ,How can i get those intrisic connections between > there regions??? Since i got 3 region active region > R1,R2 and R3,i connect R1 to R2,R3 and R2 to R1,R3 and > R3 to R1,R2, and put the driving input to an region > R1(i guess this might be the right input region,but > how can i validate this?). And i want to infer their > intrisic couplings by coupling coefficents between > them, here is result in DCM.A > -1.0000 -0.0074 -0.0810 0.0132 > 0.1593 -1.0000 -0.0383 -0.0017 > 0.4692 0.0728 -1.0000 0.0190 > 0.0539 -0.0095 -0.0453 -1.0000 > > but i foud nothing in this matrix, > > could you help me out those question?? > 1,how to infer connectivities between differe regions > 2,which region should driving input connect to? > 3,which coupling should modulatory input connect to? > > > Thank you very much ! > > > BeiLi > --- Will Penny <[log in to unmask]> 5DU}ND#: > >>Dear Li Bei, >> >>RE DCM analysis: >> >>When looking at the results of an SPM analysis >>selecting different contrasts allows you to look at >>maps of different effects. >> >>In the data on the web, it was an arbitrary choice >>to >>look at the effect of motion. You could use other >>contrasts >>to look at other effects. >> >>Looking at 'results', however, does not cause any >>VOI >>files to be written. The use of contrasts in >>extracting >>VOIs is a separate issue. >> >>Here, it is useful to specify a contrast so that >>effects of no interest can be removed. Think of this >>as a filtering. So, if I remember correctly, an >>F-contrast over the effects of Photic, Motion >>and attention was used as we are interested in >>modelling *all* of these effects with DCM. >> >>If we were only interested in modelling a subset of >>these >>effects we would have used an F-contrast spanning >>only >>that subset. >> >>RE GLM analysis: >> >>Perhaps you could simplify your design by having >>only one >>'control' variable (for A and B), instead of 3 >>(control for A, control for B, >>control for A and B). I guess its possible that [1 >>-1 0 0 0 0] >>could also be formed by a combination of the other >>regressors (which >>would result in an invalid contrast) - but >>I have'nt worked this out in detail. >> >>Best wishes, >> >>Will. >> >>Li Bei wrote: >> >> >>>Dear Penny: >>> Thank you for you help,you are so kind! >>> Sorry for bringing you so much troubles. >>> I have another 2 quesion :) >>>QUESTION 1 >>>BEGIN >>> What's the difference of VOI in chosing >> >>different >> >>>F-contrast in DCM instruction published on you >>>websit,the Result section. >>> In you example,you chosed Motion Condition >> >>,and >> >>>defined VOIs,but i found,value series of VOI are >>>different in different >>>F-contrast(Photic,Motion,Attention),why you chose >>>Motion in you example?? What will happen if you >> >>chose >> >>>Photic and define VOIs? >>>END >>> >>> >>> >>>QUESTION 2 >>>BEGIN >>>This Question is about fMRI modelling :) >>> >>>My exp. is designed like this >>>CACBCACBCACB (12 total) and A for stimuli A,B >> >>for >> >>> stimuli B,C for control >>>there is 10 scans for each character ,so there are >> >>120 >> >>>scans totally >>> >>>in fMRI Design Stage,i set the paramaters like >> >>this#: >> >>>scans per session[120] >>>Number of conditions[6] >>> >>>name for condition 1 [StimuliA] >>>Vector [10 50 90] >>> >>>name for condition 2 [StimuliAControl] //Control >>>Before Stimuli A >>>vector [0 40 80] >>> >>>name for condition 3 [StimuliB] >>>vector [30 70 110] >>> >>>name for condition 4 [StimuliBControl] //Control >>>Before Stimuli B >>>vector [20 60 100] >>> >>>name for condition 5 [StimuliA+B] //Stimuli A >> >>and >> >>>B >>>vector [10 30 50 70 90 110] >>> >>>name for condition 6 [StimuliA+BControl] //all >>>controls before stimuli >>>vector [0 20 40 60 80 100] >>> >>>then in fMRI Data stage,i chose those 120 picture >> >>, >> >>>and after Estimate#,i clicked Result >>> >>>then i chose t constract >>>I want StimuliA - StimuliAControl ,and get the >>>activation of StimuliA >>>So i wrote like this: constract vector : 1 -1 0 0 >> >>0 >> >>>0#,but the system told me that is an invalid >> >>vector! >> >>>Why? i'm so warry about this. >>>END >>> >>>Now i'm an undergraduate student, and i will apply >> >>a >> >>>foreign university in countries out of China this >>>september , I have to publish at least 2 papers >> >>before >> >>>my application in order to get a good >> >>university,Then >> >>>i have a relatively bigger change to have an >>>archivment like you've got, time is not enough for >> >>me, >> >>>so ,thank you for you help !!! >>>Thank you!! >>> >>> >>>My MSN:[log in to unmask],you can contact me if >> >>you >> >>>want to give me more help,haha ;) >>> >>> >>>Best Wishes >>> >>>BeiLi >>> >>> >>> --- Will Penny <[log in to unmask]> >> >>5DU}ND#: >> >>>>Dear Li Bei, >>>> >>>>Ideally, for a DCM analysis, you should have at >>>>least two factors. >>>> >>>>One would be a 'driving' factor - factor A, >>>>typically >>>>a series of events - though these could be >> >>blocked. >> >>>>The other would be a 'modulatory' factor - factor >> >>B, >> >>>>typically >>>>blocked. >>>> >>>>DCM is then primarily used to attribute changes in >>>>effective connectivity to factor B. >>>> >>>>You should have enough variables in your >>>>design to set up a DCM. >>>> >>>>You could specify D = ---AAABBB---AAABBB, ie >>>>A or B. This would be a driving input. >>>> >>>>Your modulatory input would then be eg. B. >>>> >>>>Best, >>>> >>>>Will. >>>> >>>> >>> >>> >>> > _________________________________________________________ > >>>Do You Yahoo!? >>>W"2aJ@=gR;AwF7VJ5DQE;"Cb7Q5gSJ >>> >> > http://cn.rd.yahoo.com/mail_cn/tag/1g/*http://cn.mail.yahoo.com/ > >>> >>-- >>William D. Penny >>Wellcome Department of Imaging Neuroscience >>University College London >> > > === message truncated === > > _________________________________________________________ > Do You Yahoo!? > W"2aJ@=gR;AwF7VJ5DQE;"Cb7Q5gSJ > http://cn.rd.yahoo.com/mail_cn/tag/1g/*http://cn.mail.yahoo.com/ > > -- William D. Penny Wellcome Department of Imaging Neuroscience University College London 12 Queen Square London WC1N 3BG Tel: 020 7833 7475 FAX: 020 7813 1420 Email: [log in to unmask] URL: http://www.fil.ion.ucl.ac.uk/~wpenny/