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Hi - this is a huge fractional age range - if you have checked your registrations are all ok then I would think probably that this result is valid. Cheers. On 13 May 2010, at 09:16, Mark Shen wrote: > I do not think the correlation could possibly be so high that the correlation between Group1's FA and age survives at corrected 1-p=.995 for nearly every voxel (#46000) in the mean_FA_skeleton. Group2's correlation between FA and age is a little more reasonable but still very high (corrected 1-p=.96 for 4600 voxels). > > Perhaps I should elaborate that Group1 has a developmental disorder and Group2 is controls, both ranging in age from 2-5 years old. I include the .con and .mat files below for clarification. Thanks for all your assistance and patience! > > .con file > > /NumWaves 4 > /NumContrasts 2 > /PPheights 1 1 > /Matrix > 0 0 1 0 > 0 0 0 1 > > .mat file > > /NumWaves 4 > /NumPoints 76 > /PPheights 2.72 2.72 > /Matrix > 1 0 0.49 0 > 1 0 1.24 0 > 1 0 0.01 0 > 1 0 -0.60 0 > 1 0 0.66 0 > 1 0 1.21 0 > 1 0 1.30 0 > 1 0 1.42 0 > 1 0 0.59 0 > 1 0 -0.06 0 > 1 0 0.44 0 > 1 0 0.68 0 > 1 0 0.18 0 > 1 0 0.33 0 > 1 0 0.19 0 > 1 0 0.46 0 > 1 0 -0.19 0 > 1 0 -0.47 0 > 1 0 0.27 0 > 1 0 -0.29 0 > 1 0 0.60 0 > 1 0 0.05 0 > 1 0 -0.34 0 > 1 0 -0.41 0 > 1 0 -0.43 0 > 1 0 -0.45 0 > 1 0 0.02 0 > 1 0 0.16 0 > 1 0 0.43 0 > 1 0 0.08 0 > 1 0 -0.18 0 > 1 0 -0.49 0 > 1 0 0.36 0 > 1 0 -0.34 0 > 1 0 0.35 0 > 1 0 1.16 0 > 1 0 -0.08 0 > 1 0 0.11 0 > 1 0 -0.87 0 > 1 0 -1.02 0 > 1 0 0.21 0 > 1 0 -0.62 0 > 1 0 0.37 0 > 1 0 -0.60 0 > 1 0 -0.22 0 > 1 0 -0.40 0 > 1 0 -0.90 0 > 1 0 -0.91 0 > 1 0 -0.70 0 > 1 0 -0.52 0 > 1 0 -0.73 0 > 1 0 -0.81 0 > 1 0 -0.50 0 > 0 1 0 -0.31 > 0 1 0 -0.22 > 0 1 0 1.70 > 0 1 0 1.29 > 0 1 0 0.93 > 0 1 0 0.16 > 0 1 0 -0.46 > 0 1 0 -0.32 > 0 1 0 -0.02 > 0 1 0 -0.54 > 0 1 0 0.14 > 0 1 0 -0.16 > 0 1 0 -0.98 > 0 1 0 -0.15 > 0 1 0 0.28 > 0 1 0 -0.61 > 0 1 0 -0.12 > 0 1 0 -0.26 > 0 1 0 0.24 > 0 1 0 -0.03 > 0 1 0 -0.67 > 0 1 0 0.07 > 0 1 0 -0.04 > > > > On May 13, 2010, at 12:58 AM, Stephen Smith wrote: > > Hi > > On 13 May 2010, at 08:39, Mark Shen wrote: > >> Thank you for your response. I confirmed that age is indeed demeaned (sum of each group's demeaned age equals 0, average equals 0). The age should be demeaned within each group and then padded with zeros, correct? > > Correct > >> And the PPheights should be the difference between the max and min demeaned value of whichever group gives the highest difference? > > Sure - though this isn't used by randomise anyway. > > So - is it possible that you have a strong widespread age correlation then? > > Cheers. > > > > >> >> Thanks again for your expertise. >> >> >> On May 12, 2010, at 11:11 PM, Stephen Smith wrote: >> >> Getting weird distributions in t that don't look like they have a sensible amount of null values in them (i.e. roughly gaussian mean 0 std 1) can be caused by two things in general: >> >> - You don't have much null effect in the data - e.g. in VBM or TBSS when you have a global / widespread correlation against your model >> >> - There's a problem with the data not being demeaned but the model being zero mean, etc. >> >> Cheers. >> >> >> >> >> On 12 May 2010, at 16:16, Mark Shen wrote: >> >>> Hi, I have a follow-up question to this post. Are the outputs from this correlational analysis (below) still interpreted as t-statistic for the correlation between each group and age, the corresponding incorrect 1-p value, and the corresponding corrected 1-p value? If so, I have some strange results I would like feedback on. >>> >>> contrasts: >>>> [0 0 1 0 0 0] -- patients >>>> [0 0 0 1 0 0] -- controls >>> >>> >>> The resulting tstat map looks randomly distributed around .8 and has a range of -2.5-4. The p stat map ranges from 0-1 but has all frequency (#6000 voxels) near 1. The corrp map ranges from 0-1 and has the highest frequency near 1 (#2000 voxels). >>> >>> Does this seem plausible? How could the 1-p value for the correlation between age and FA be so highly significant for almost every voxel? >>> >>> Thank you! >>> Mark >>> >>> On May 6, 2010, at 4:51 AM, DRC SPM wrote: >>> >>> Hi Amelia, >>> >>> If your design is: >>> >>>> EV1=Patients >>>> EV2=Controls >>>> EV3=Patients_age_demeaned (I subtracted the mean age of all patients from >>>> each patient’s age. I put 0s for all Controls in this EV.) >>>> EV4=Controls_age_demeaned ((I subtracted the mean age of all controls from >>>> each controls’s age. I put 0s for all Patients in this EV.) >>>> EV5=gender_demeaned (I subtracted the mean gender of all subjects - both >>>> patients and controls - from each subject's gender.) >>>> EV6=handedness_demeaned (I subtracted the mean handnessness of all subject - >>>> both patients and controls - from each subject's handedness.) >>> >>> Then you can test for relationships with age within each group with: >>> [0 0 1 0 0 0] -- patients >>> [0 0 0 1 0 0] -- controls >>> and test for the slope with age being steeper in one group than the other with >>> [0 0 1 -1 0 0] -- patients > controls >>> [0 0 -1 1 0 0] -- controls > patients >>> where an F-test over either of these will test for different age >>> slopes between the groups. >>> >>> Note that these can't be interpreted as stronger or weaker >>> age-correlations between the groups. In a simple or multiple >>> regression model, a t-contrast with a single 1 over a variable is >>> equivalent to testing the (partial) correlation with that variable, >>> but in more complicated models, you can't assume intuitively similar >>> equivalences. For example, you could have a steeper slope in patients, >>> but a lower correlation, if the patients are more variable around the >>> slope; the contrast above tests just for the steeper slope, not for >>> the different correlation. >>> >>> I hope that helps, >>> Ged >>> >> >> >> --------------------------------------------------------------------------- >> 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 >> --------------------------------------------------------------------------- >> >> >> >> > > > --------------------------------------------------------------------------- > 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 > --------------------------------------------------------------------------- > > > > --------------------------------------------------------------------------- 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 ---------------------------------------------------------------------------