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Thank you Anderson! I've got it. Best, Jun On 2019/04/08 4:51, Anderson M. Winkler wrote: > Hi Jun, > > 1) Yes. > 2) Yes, but here it isn't totally random because if there is a > reference map already defined, e.g., for the dual regression, then > positive and negative between-subject effects will be in relation to > that map. So, it isn't random as if tossing a coin. > 3) Yes, but same as above for (2). > > All the best, > > Anderson > > > On Wed, 3 Apr 2019 at 20:39, Jun_Miyata > <[log in to unmask] > <mailto:[log in to unmask]>> wrote: > > Dear Anderson, > > Sorry for interrupting. I have a question about your reply below: >> That is, like with the usual 2D-ICA, tri-dimensional >> decomposition cannot preserve the relative variances across >> components, nor their sign/direction. > > I understand this as below: > 1. we cannot compare component loadings of each subject "between" > two components. > 2. we can tell if a behavior measure is correlated with subjects' > loadings of a component, but cannot tell if it's positive or > negative correlation. > 3. The same as point 2., we can tell if a component loadings are > different between two group of subjects (groups A and B), but > cannot tell if it's A>B or A<B. > > Are these true? > > Thank you in advance, > Jun > > > On 2019/04/04 0:24, Anderson M. Winkler wrote: >> Hi Leonardo, >> >> Please see below: >> >> >> On Fri, 29 Mar 2019 at 18:37, Leonardo Tozzi <[log in to unmask] >> <mailto:[log in to unmask]>> wrote: >> >> Dear Experts, >> >> I have a large number of subjects that have undergone the >> exact same fMRI task. I would like to run an ICA using >> MELODIC to identify components associated with the task >> design across all subjects. In particular, I would be >> interested in the variation of the components in the subject >> domain and, from what I understand, tensor ICA is suited for >> this purpose, since it returns a subject*component matrix. >> >> I have two questions. First of all, how can one intuitively >> conceptualize the elements of this matrix? Is this a >> weighting factor that represents the individual variation in >> this component at the subject level? >> >> >> Yes. >> >> Could I then, for example, choose a component that is >> associated with my task design and correlate the >> corresponding values of this matrix (one per subject) to task >> performance or a questionnaire? >> >> >> Yes. However, you should not compare component strengths in >> absolute values. That is, like with the usual 2D-ICA, >> tri-dimensional decomposition cannot preserve the relative >> variances across components, nor their sign/direction. As long as >> you look into correlations or t-statistics, and do not make >> inferences on the direction of the effects, results should be >> valid and interpretable. >> >> If you look into more than one component, remember to correct for >> multiple testing, and note that independence is maximal on the >> spatial domain, but not on temporal or subject domains, such that >> Bonferroni will be conservative. >> >> The second questions pertains to dual regression. From what I >> understand, it is common practice to use this procedure to >> assess the differences in spatial extent of components >> between, for example, two groups. However, if I focused on >> the individual score mentioned above, would it be necessary >> to run a dual regression at all? From my understanding, >> running dual regression would “just” tell me where in the >> brain the association between the group component and the >> individual’s timeseries is highest, but I am not sure how the >> decomposition in 3 matrices of TICA (instead of 2) factors in >> this procedure (or if it is at all possible). I would imagine >> that the dual regression would allow me to identify the >> voxels that show the best fit to the component, while >> accounting at the same time for the subject score. Is this >> correct? >> >> >> You can still do the dual regression. It will give something that >> tensor ICA doesn't yield: subject-level spatial maps. These can >> then be subjected to statistical analysis, allowing for >> localizing power. Interpretation is the same as in the usual >> dual-regression, except noting that differently than in the >> temporal concatenation ICA, in which a set of components that >> have spatial features that are common across participants is >> produced, here a set of components that have temporal AND spatial >> features that are common across participant is produced. >> >> Also, I don't think the dual_regression script will run out of >> the box. You'd need to tweak it yourself... >> >> All the best, >> >> Anderson >> >> >> Thank you very much for your help, >> >> Leonardo Tozzi, MD, PhD >> >> Williams PanLab | Postdoctoral Fellow >> >> Stanford University | 401 Quarry Rd >> >> [log in to unmask] <mailto:[log in to unmask]> | (650) 5615738 >> >> >> ------------------------------------------------------------------------ >> >> To unsubscribe from the FSL list, click the following link: >> https://www.jiscmail.ac.uk/cgi-bin/webadmin?SUBED1=FSL&A=1 >> >> >> ------------------------------------------------------------------------ >> >> To unsubscribe from the FSL list, click the following link: >> https://www.jiscmail.ac.uk/cgi-bin/webadmin?SUBED1=FSL&A=1 >> > > > ------------------------------------------------------------------------ > > To unsubscribe from the FSL list, click the following link: > https://www.jiscmail.ac.uk/cgi-bin/webadmin?SUBED1=FSL&A=1 > > > ------------------------------------------------------------------------ > > To unsubscribe from the FSL list, click the following link: > https://www.jiscmail.ac.uk/cgi-bin/webadmin?SUBED1=FSL&A=1 > ######################################################################## To unsubscribe from the FSL list, click the following link: https://www.jiscmail.ac.uk/cgi-bin/webadmin?SUBED1=FSL&A=1