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Christian, I tried this first regression step on a group dataset I had already run through melodic, against a subject's 4D filtered_func_data in global space 2x2x2 mm resolution, and was finding it quite memory intensive. It was using up to 7GB of memory to run this application, is this what you would expect? Chris On Nov 14 2008, Christian F. Beckmann wrote: >Hi Jim, > >For de-noising to work you need subject-specific spatial maps and time >courses. You can get them quite easily by regressing the subject >specific data sets against the melodic_IC file and then re-regressing >the same data set against the output from the first regression: > >(i) fsl_glm -i filtered_func_X -d group_melodic_IC -o timecourses_X > >(ii) fsl_glm -i filtered_func_X -d timecourses_X -o maps_X > >After this you can simply follow the normal melodic instructions and >use timecourses_X and maps_X for each data set X > >hth >Christian > > > > >On 11 Nov 2008, at 20:18, James Porter wrote: > >> Hello- >> >> On the Melodic instructions page there are clear instructions on how >> to denoise a functional volume at the single-subject single-scan >> level. However, if one wants to denoise a group-wise data set after >> running tensor-ICA, is there an equivalent procedure? >> >> -- >> --------- >> Jim Porter >> Graduate Student >> Clinical Science & Psychopathology Research >> University of Minnesota > > >_______________________________________________ >Christian F. Beckmann, DPhil >Senior Lecturer, Clinical Neuroscience Department >Division of Neuroscience and Mental Health >Imperial College London, Hammersmith Campus >Rm 419, Burlington Danes Bldg, Du Cane Road, London W12 0NN, UK >Tel.: +44 (0)20 7594 6685 --- Fax: +44 (0)20 7594 6548 >Email: [log in to unmask] >http://www.imperial.ac.uk/medicine/people/c.beckmann/ > >Senior Research Fellow, FMRIB Centre >University of Oxford >JR Hospital - Oxford OX3 9DU >Tel.: +44 (0)1865 222551 --- Fax: +44 (0)1865 222717 >Email: [log in to unmask] >http://www.fmrib.ox.ac.uk/~beckmann >