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

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Presentation on theme: "Haskins fMRI Workshop Part II: Single Subject Analysis - Event & Block Designs."— Presentation transcript:

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

2 Analysis Strategy Overview 1)“massively univariate” approach 2)two-stage analysis: a)single-subject regression on time-course data b)across-subject t-test on subject effects c)why? simpler...

3 Block & Event-Related Designs Block designs 1)task or condition alternates at ~20 second intervals 2)analysis looks for differences in the mean response level across the entire block Event-related designs 1)random order of stimulus types 2)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: 1)block type or event type (experimental conditions)... and several “nuisance” variables, or variables of “no” interest (aka covariates) 1)main effect-off-zero (constant term) 2)run-by-run mean effect 3)time trends - first, second, third order 4)(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”). condBcontrast weight words lines7.4-1 contrast formula: [+1 * 18.1] + [-1 * 7.4] = 10.7 = “contrast value” =...just another B-weight!

5 Block Design; Simulated Data nonwordswords time model data

6 Block Design Example

7 Block Design; Simulated Data

8 Block Design; Simulated Data (2)

9 Case Mixing Exp. (design) 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

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

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

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

27 Multiple Regression (3): Reference Waveform Regression Friston et al 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 Event Regressors For Cond 1 Event Regressors For Cond Peristimulus Time

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

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

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

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

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

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... 1)ITIs down to ~2-3 seconds 2)varied ITIs 3)“null” trials (extra long trials) 4)commonly need ~40 trials per condition; > 8 conditions 5)order matters! 6)“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 + house &&&&& 1000 msec 500/1500 msec 25/50/100 msec 1000 msec

51 titration of stimulus duration (2)


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