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RFX in SPM5

RFX in SPM5. Floris de Lange florisdelange@gmail.com. RFX Options. Conditions are MI LH (press left foot) and MI RH (press right foot); 8 subjects; threshold used is p<0.001 uncorrected, k>20 (arbitrary) Methods used: One-sample T-test on difference images MI LH>MI RH

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RFX in SPM5

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  1. RFX in SPM5 Floris de Lange florisdelange@gmail.com

  2. RFX Options

  3. Conditions are MI LH (press left foot) and MI RH (press right foot); 8 subjects; threshold used is p<0.001 uncorrected, k>20 (arbitrary) • Methods used: • One-sample T-test on difference images MI LH>MI RH • Paired-samples T-test on MI LH and MI RH • Measurements assumed independent • Measurements assumed dependent • Two-samples T-test on MI LH and MI RH • Multiple regression analysis on MI LH and MI RH • Full factorial • Flexible factorial Compare 2 conditions

  4. The gold standard: one-sample T-test

  5. Paired T-test: dependence/indepence Independent: error covariance matrix = identity matrix (check SPM.xVi.V!) Dependent: error covariance matrix will be estimated (check SPM.xVi.V!)

  6. Paired-samples T-test dep.: same

  7. Dependence/independence doesn’t make a difference here, because there’s only one sample to estimate covariance from Paired-samples T-test indep.: same

  8. Multiple regression analysis: same = identical

  9. Two-samples T test indep: worse Degrees of freedom ↑ Variance term ↑

  10. the correlation between the variance of the subjects in the first group and those in the second group is estimated • this reduces the error term Two-samples T test dep: better

  11. Two-samples T test: dep vs indep Dependent measures Independent measures

  12. Two-samples T test: con images = Dependent measures Independent measures

  13. Two-samples T test: ResMS images < Dependent measures Independent measures Error terms is reduced for dependent measures by modelling the dependencies

  14. Full factorial dep. = 2-sample T dep

  15. Full factorial indep. = 2-sample T indep

  16. Flex factorial dep. = 2-sample T dep

  17. Flex factorial indep = 2-sample T indep

  18. There are two types of models: • Models that specify the subject factor (e.g., one-sample, paired-samples, MRA if you specify the factor yourself) • Models that estimate the subject factor (e.g., two-samples T-test, full factorial, flexible factorial; measurements are dependent) • If you don’t specify the subject factor, but also don’t estimate the error covariance, you are likely to shoot yourself in the foot because the errors will be assumed to be independent, and simply added, leading to much higher estimates of the error term Summary

  19. It can be statistically beneficial to specify the model as a “between-subjects” model without modelling subject, but instead estimating the subject-induced regularities by specifying that measures may be dependent • SPM5 manual suggests to do analyses this way • But is it valid? Aren’t df’s inflated? Is it valid to use 2-sample T test dep? SPM5 manual

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