dset 1 1 1 stats.sb04.betas+tlrc

More documents

Recommendations

Info

-30-• STEP 5: Perform a regression analysis on Subject sb23’s datawith 3dDeconvolve. What is the difference between regular linear regression and deconvolution?With linear regression, the hemodynamic response is assumed.With deconvolution, the hemodynamic response is not assumed. Instead, it iscomputed by 3dDeconvolve from the data.• For this dataset, we will go with the linear regression option in3dDeconvolve. BLOCK(30,1) was the response model chosen for this analysis. Why?BLOCK: The design of this experiment is BLOCK, i.e., there are blockedintervals of stimulus presentations (ON), followed by blocked intervals of thecontrol condition (OFF). This design differs from eventrelated, whereexperimental stimuli and controls are presented randomly throughout theexperiment.30: Each block lasts 30 seconds 1: The response function will have a peak of 1
-31-• 3dDeconvolve command Part 19 input datasets3dDeconvolve -input pb04.$subj.r??.scale+orig.HEAD \-polort 2 \-mask full_mask.$subj+orig \-num_stimts 15 \Continued on next page… see input dataset list by typing: echo pb04.$subj.r??.scale+orig.HEAD note that there should be 9 such datasets, for the 9 runs the .HEAD suffix is necessary to do the wildcard expansion with '??' -mask: Use mask to avoid computation on zerovalued time series -num_stimts 15: 9 regressors + 6 motion parameters = 15 input stimulus timeseries the 9 regressors of interests are given as timing files, via -stim_times the 9 motion parameters are given as actual regressors, via -stim_file
Page 1 and 2: -1-AFNI Soup to Nuts:How to Analyze
Page 3 and 4: -3-• The Experiment : Cognitive T
Page 5 and 6: -5-• Analysis Steps: Part I: Proc
Page 7 and 8: -7- LinebyLine Explanation of s
Page 9 and 10: -9-• PART I ⇒ Process Data for
Page 11 and 12: -11-• STEP 0 (tcat): Apply 3dTcat
Page 13 and 14: -13- Subject sb23’s outlier files
Page 15 and 16: -15-• Next, perform temporal alig
Page 17 and 18: -17-• STEP 2: Register the volume
Page 19 and 20: -19- view the registration paramete
Page 21 and 22: -21-results from 3dmerge:pb02.sb23.
Page 23 and 24: -23- next, take the union of the ru
Page 25 and 26: -25-• STEP 4: Scaling the Data
Page 27 and 28: -27- Scale the Data:foreach run ( $
Page 29: -29- compare EPI images from before
Page 33 and 34: -33-• 3dDeconvolve command Par
Page 35 and 36: -35-• There are a many reasons wh
Page 37 and 38: -37-• Create ideal response files
Page 39 and 40: -39-• Generate a script that the
Page 41 and 42: -41-• Now plot the all_runs datas
Page 43 and 44: -43- Result: One dataset for each o
Page 45 and 46: -45-• PART 2 ⇒ Run Group Analys
Page 47 and 48: -47-• 3dANOVA3 Command Part 2-
Page 49 and 50: -49- -adiff: Performs contrasts bet
Page 51 and 52: -51- In class -- Let s run the ANOV
Page 53 and 54: -53- Brain areas corresponding to
Page 55: -55-• Many thanks to NIMH LBC for

dset 1 1 1 stats.sb04.betas+tlrc

You also want an ePaper? Increase the reach of your titles

Delete template?

Save as template?