1 / 51

Haskins fMRI Workshop Part II: Single Subject Analysis - Event & Block Designs

Haskins fMRI Workshop Part II: Single Subject Analysis - Event & Block Designs. Analysis Strategy Overview. “massively univariate” approach two-stage analysis: single-subject regression on time-course data across-subject t-test on subject effects why? simpler. Block & Event-Related Designs.

nuncio
Download Presentation

Haskins fMRI Workshop Part II: Single Subject Analysis - Event & Block Designs

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Haskins fMRI WorkshopPart II:Single Subject Analysis - Event & Block Designs

  2. Analysis Strategy Overview • “massively univariate” approach • two-stage analysis: • single-subject regression on time-course data • across-subject t-test on subject effects • why? simpler...

  3. Block & Event-Related Designs Block designs task or condition alternates at ~20 second intervals analysis looks for differences in the mean response level across the entire block Event-related designs random order of stimulus types analysis extracts the simple “evoked” response to each stimulus type; then assesses differences among types

  4. Single-subject regression models & contrasts A regression model is run, predicting the activation values as a function of several predictor variables. Some are interesting: block type or event type (experimental conditions) ... and several “nuisance” variables, or variables of “no” interest (aka covariates) main effect-off-zero (constant term) run-by-run mean effect time trends - first, second, third order (motion correction parameters?) The goal is to obtain B-weights (regression parameter estimates) for each effect of interest. These are additionally combined using linear contrasts to implement simple “subtraction” analyses and to look for more complicated patterns of activation across tasks (“profile analysis”). cond B contrast weight words 18.1 +1 lines 7.4 -1 contrast formula: [+1 * 18.1] + [-1 * 7.4] = 10.7 = “contrast value” = ...just another B-weight!

  5. Block Design; Simulated Data model nonwords words time data

  6. Block Design Example

  7. Block Design; Simulated Data

  8. Block Design; Simulated Data (2)

  9. Subjects get 10 scan runs, each 5:40 long Each run, they see 5 blocks of baseline trials: //*\ or /\\/ and 4 blocks of lexical decision trials: 1 high frequency unmixed case 2 low frequency unmixed case 3 high frequency mixed case 4 low frequency unmixed case charm or cHaRm Case MixingExp. (design)

  10. Case Mixing Exp. (design) 1 baseline /\*/ 2 HF unmixed case 3 LF unmixed case 4 HF mIxEd CaSe 5 LF mIxEd CaSe

  11. Case Mixing Exp. int c1 c2 c3 c4 c5 image number 1 baseline /\*/ 2 HF unmixed case 3 LF unmixed case 4 HF mIxEd CaSe 5 LF mIxEd CaSe

  12. Case Mixing Exp. (design) 1 baseline /\*/ 2 HF unmixed case 3 LF unmixed case 4 HF mIxEd CaSe 5 LF mIxEd CaSe

  13. Case Mixing Exp. (design) 1 baseline /\*/ 2 HF unmixed case 3 LF unmixed case 4 HF mIxEd CaSe 5 LF mIxEd CaSe

  14. Case Mixing Exp. (design) 1 baseline /\*/ 2 HF unmixed case 3 LF unmixed case 4 HF mIxEd CaSe 5 LF mIxEd CaSe

  15. Case MixingExp. (1) single-subject words - lines 1 baseline /\*/ 2 HF unmixed case 3 LF unmixed case 4 HF mIxEd CaSe 5 LF mIxEd CaSe

  16. Case MixingExp. (2) single subject mixed - unmixed 1 baseline /\*/ 2 HF unmixed case 3 LF unmixed case 4 HF mIxEd CaSe 5 LF mIxEd CaSe

  17. Case MixingExp. (12) across subject high frequency words mixed - unmixed 1 baseline /\*/ 2 HF unmixed case 3 LF unmixed case 4 HF mIxEd CaSe 5 LF mIxEd CaSe

  18. Case MixingExp. (13) across subject low frequency words mixed - unmixed 1 baseline /\*/ 2 HF unmixed case 3 LF unmixed case 4 HF mIxEd CaSe 5 LF mIxEd CaSe

  19. Case Mixing (14): Time effects in IFG

  20. Case Mixing (15): Time effects in OT

  21. Event-Related Designs, Two Analyses 1) Using a priori predicted response functions a) long intertrial interval designs b) Reference waveform regression 2) Estimating the actual response a) simple averaging b) delta function regression (FIR, finite impulse response) 3) Some examples

  22. Simulated Hemodynamic Response (1) mean = 5000 sd = 100 effect size 0-1%, 0-50 points Gamma function tau=1.08; n=3; delay=3

  23. Simulated Hemodynamic Response (2) Noise SD = 0 Noise SD = 10 Noise SD = 100

  24. Event-Related Designs, Two Analyses 1) Using a priori predicted response functions a) long intertrial interval designs b) Reference waveform regression 2) Estimating the actual response a) simple averaging b) Delta Function Regression 3) Some examples

  25. cf. Ni et al., (2000) Long Intertrial Intervals, Single Condition Simulated Data Event Regressor

  26. Long Intertrial Intervals, Multiple Conditions Simulated Data Cond 1 Cond 2 Event Regressors Cond 1 Cond 2

  27. Multiple Regression (3):Reference Waveform Regression Friston et al. 1994 int C1 C2 Cond 1 Cond 2

  28. Variability of the HRF Aguirre et al., 1998

  29. Event-Related Designs, Two Analyses 1) Using a priori predicted response functions a) long intertrial interval designs b) reference waveform regression 2) Estimating the actual response a) simple averaging b) delta function regression 3) Some examples

  30. Estimating the Response (1):Simple Averaging, No Overlap Cond 1 Cond 2

  31. Estimating the Response (2):Simple Averaging, With Overlap Cond 1 Cond 2

  32. Estimating the Response (3):Simple Subtraction with Overlap Dale & Buckner, 1997

  33. Delta Function Regression (1) Meizin et al., 2000

  34. Delta Function Regression (2) Cond 1 Cond 2 -1 0 +1 +2 +3 . . . +15 Event Regressors For Cond 1 Peristimulus Time Event Regressors For Cond 2

  35. Delta Function Regression (3) Cond 1 Cond 2___ Cond 1 Cond 2

  36. Delta Function Regression (4):Evoked Responses Estimated Response Image Intensity Image Intensity Peristimulus Time

  37. Delta Function Regression (5): Overlapping Responses Cond 1 Cond 2___ Cond 1 Cond 2

  38. Delta Function Regression (6):Gamma Fitting Estimated Response & Gamma Fits Image Intensity Image Intensity Peristimulus Time

  39. Delta Function Regression (4):“Blocklet” Analysis Estimated Response Activation Timepoints [+3 to +8] Baseline Timepoints [-1 to 0] Image Intensity Image Intensity Peristimulus Time

  40. Compare & Contrast... Reference Waveform Regression 1) most designs are analyzable 2) stronger power 3) biased when reference <> actual Delta Function Regression 1) some designs not analyzable 2) weaker power 3) unbiased measure of temporal response

  41. Event-related design details... ITIs down to ~2-3 seconds varied ITIs “null” trials (extra long trials) commonly need ~40 trials per condition; > 8 conditions order matters! “stroboscopic” sampling: odds versus even timepoints

  42. CRM experiment . auditory words: 1 new words 2 old words

  43. CRM experiment . auditory words: 1 new words 2 old words

  44. CRM experiment . auditory words: 1 new words 2 old words

  45. CRM experiment . auditory words: 1 new words 2 old words

  46. CRM experiment . auditory words: 1 new words 2 old words

  47. CRM experiment . auditory words: 1 new words 2 old words

  48. CRM experiment . auditory words: 1 new words 2 old words

  49. CRM experiment . auditory words: 1 new words 2 old words

  50. titration of stimulus duration 1000 msec + 500/1500 msec 25/50/100 msec house 1000 msec &&&&&

More Related