dset 1 1 1 stats.sb04.betas+tlrc

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-4- Illustration of Stimulus Conditions:AFFECTNegative Positive NeutralTelephone“Your project is lame,just like you!”“You are the bestproject leader!”“You work on theproject.”CATE E-mailGORY Face-to-Face"Ugh, your hairis hideous!"“I curse the day I metyou!”"Your newhaircut looksawesome!"“I feel lucky to haveyou in my life.”"You got ahaircut."“I know who youare.”●Data Collected:●1 Anatomical (MPRAGE) dataset for each subject●●124 axial slicesvoxel dimensions = 0.938 x 0.938 x 1.2 mm●9 Time Series (EPI) datasets for each subject●34 axial slices x 67 volumes = 2278 slices per run●TR = 3 sec; voxel dimensions = 3.75 x 3.75 x 3.5 mm●Sample size, n=16 (all right handed)
-5-• Analysis Steps: Part I: Process data for each individual subject (using afni_proc.py)Preprocess subjects’ data ⇒ many steps involved here…Run regression analysis on each subject’s data 3dDeconvolve Part II: Run group analysis warp results to standard space3way Analysis of Variance (ANOVA) 3dANOVA3• Category (3) x Affect (3) x Subjects (16) => 3way ANOVA• Class work for Part I: view the original data by running afni from the subject sb23/ directory then view output data from the sb23.blk.results/ directorycd AFNI_data4/sb23lsafni &
Page 1 and 2: -1-AFNI Soup to Nuts:How to Analyze
Page 3: -3-• The Experiment : Cognitive T
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 and 30: -29- compare EPI images from before
Page 31 and 32: -31-• 3dDeconvolve command Par
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
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dset 1 1 1 stats.sb04.betas+tlrc

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