Hi, Matthias, On Wed, Oct 7, 2009 at 8:44 AM, Matthias Ekman <[email protected]> wrote: > Hi, > > mh - may be I am wrong here, but from my point of view, high-pass > filtering AND detrending is not "necessary". What happens if you > zscore/detrend your data without step (4). My guess would be, that this > somehow strange effect of negative correlation will disappear.
I think I may have written the wrong line of code earlier....if I just use 'detrend' with 'model=constant', then that should not be a problem, correct? And when I do that, then I still get this same strange effect. > > Bests, > Matthias > > John Clithero schrieb: >> Hi, >> >> Looks like my email with the voxel image is waiting for moderator >> approval, so here is my updated response without the picture: >> >> On Tue, Oct 6, 2009 at 4:39 PM, Yaroslav Halchenko >> <[email protected]> wrote: >>> On Tue, 06 Oct 2009, John Clithero wrote: >>>> The 'wb_file' is func data with the relevant timepoints... it has had >>>> some preprocessing done in FSL (motion correction, slice timing, brain >>>> extraction). The ROI mask is a whole brain mask. It seems (I hope) >>>> that the NiftiDataset is being put together correctly. >>> wild guess - may be while doing FSL preprocessing you've done some >>> intensity normalization? >> Here (sorry if I didn't list them) are the preprocessing options I used in >> FSL >> (1) brain extraction >> (2) motion correction >> (3) slice-timing correction >> (4) high-pass filtering >> Added this just now: >> As far as I know, FSL does the following (from website and their >> course lectures): >> "Scale each 4D dataset by a single value to get the overall 4D mean >> (dotted line) to be the same" >> The intensitiy normalization was off during preprocessing (as FSL >> recommends). >> Is this what you were asking about? >> >>> >>>> This perfect negative correlation occurs even if I feed in arbitrary >>>> labels to the NiftiDataset, so there must be some sort of error in how >>>> I'm using detrend and/or score?? I am guessing this is my erros since >>>> the raw feature data looks fine to me. >>>> This perfect negative correlation also occurs after just implementing >>>> "zscore" or "detrend", although obviously the values are different. >>> so, they aren't present in just loaded dataset but if you do zscore or >>> detrend -- they come? >> >> If I break data in half (A trials vs B trials, or random) before using >> zscore or detrend, I generally see strong positive correlation (some >> voxels are just more active than others across the experiment...this >> makes sense). But, once I use zscore or detrend....then goes to >> perfect negative correlation (when looking at meanA vs meanB). >> Each voxel after zscore, as it was said early, will have a mean of >> zero across all trials...this is true for all the voxels. >> >>> could you just plot 1 voxel (which later carries perfect correlation) >>> before/after detrend? >>> >> This is waiting for moderator approval, I guess. >> Thanks for the help. >> >> Cheers, >> John >> >> >>> -- >>> .-. >>> =------------------------------ /v\ ----------------------------= >>> Keep in touch // \\ (yoh@|www.)onerussian.com >>> Yaroslav Halchenko /( )\ ICQ#: 60653192 >>> Linux User ^^-^^ [175555] >>> >>> >>> >>> _______________________________________________ >>> Pkg-ExpPsy-PyMVPA mailing list >>> [email protected] >>> http://lists.alioth.debian.org/mailman/listinfo/pkg-exppsy-pymvpa >>> >> >> _______________________________________________ >> Pkg-ExpPsy-PyMVPA mailing list >> [email protected] >> http://lists.alioth.debian.org/mailman/listinfo/pkg-exppsy-pymvpa > _______________________________________________ Pkg-ExpPsy-PyMVPA mailing list [email protected] http://lists.alioth.debian.org/mailman/listinfo/pkg-exppsy-pymvpa
