Hi Steve,Thank you for your quick reply.Dear FSLers,I read the TFM paper with great interest. I would learn how to generate TFMs using our resting-state fMRI data but cannot figure out how it could be done at the last step. The fastICA may generate group-wise 21 TFM time series using 142-node time series data, which was calculated by multi-session temporal concatenation mode of melodic. How this group-wise TFM time courses can be fed into regression of original single session data?We used dual-regression (see FSL wiki) to get all the subject timecourses corresponding to the group-ICA spatial maps, loaded them into Matlab (you can use FSLNets for this), removed the "bad" group-level components, demeaned all time series, concatenated them across subjects, and fed them into temporal ICA using FastICA (find on the web).Here, I suppose FastICA will generate TFM time courses which span across sessions/subjects. Then how can we proceed to generate TFM maps? Is it correct if I split the TFM time series into each of single session/subjects, demean it, and regress it to the original data, and then combine maps across sessions/subjects?
TakuyaNote that it is really hard to get reliable components from temporal ICA applied to FMRI time series - you need LOTS of timepoints (we only just had enough for the published paper) and very clean data.Steve.Can it be done directly using fsl_glm? and/or Should I use weighting matrix (At) to generate single session TFMs using single session 142 spatial nodes? Could anyone give me some hints to do this?Thank you in advance.Best regards,Takuya--
Takuya Hayashi, MD, PhD
Functional Architecture Imaging Unit
RIKEN Center for Life Science Technologies, Kobe, Japan
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Stephen M. Smith, Professor of Biomedical Engineering
Associate Director, Oxford University FMRIB Centre
FMRIB, JR Hospital, Headington, Oxford OX3 9DU, UK
+44 (0) 1865 222726 (fax 222717)
[log in to unmask] http://www.fmrib.ox.ac.uk/~steve
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