dset 1 1 1 stats.sb04.betas+tlrc

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-34-• 3dDeconvolve command Part 4-gltsym 'SYM: +eneg -fneg' \-glt_label 1 eneg_vs_fneg \-gltsym 'SYM: 0.5*fneg 0.5*fpos -1.0*fneu' \-glt_label 2 face_contrast \-gltsym 'SYM: tpos epos fpos -tneg -eneg -fneg' \-glt_label 3 pos_vs_neg \-fout -tout -x1D X.xmat.x1D -xjpeg X.jpg \-fitts fitts.$subj \-bucket stats.$subjEnd of command -gltsym: General linear tests are written out symbolically (and easily!) on thecommand line, e.g., 'SYM: +eneg -fneg , which replaces something like:0 0 0 0 0 0 0 0 & 1 0 0 -1 0 0 0 00 & 0 -fout, -tout: output F and tstats for each test -x1D: output the X matrix in a 1D text file (with NIML header), X.xmat.1D -xjpeg: also output the X matrix as a JPEG image, X.jpg -fitts: output the time series of the model fit in fitts.sb23.blk+orig -bucket: output all beta weights, glts and statistics on them into on them intoone bucket dataset, stats.sb23.blk+orig
-35-• There are a many reasons why 3dDeconvolve might fail: duplicate regressors missing (incorrectly named) or incorrectly formatted stimulus timing files incorrect usage of extra options, such as gltsymsome options are were given “as is” to 3dDeconvolve by afni_proc.py• via regress_opts_3dD insufficient memory to process the dataif ( $status != 0 ) thenecho '---------------------------------------'echo '** 3dDeconvolve error, failing...'echo ' (consider the file 3dDeconvolve.err)'exitendif If 3dDeconvolve fails, it sets the $status shell variable to 1. That will allow the script todetect the failure and terminate early, so the user can see the errors.3dDeconvolve stores any errors in 3dDeconvolve.err, for later review. Some errors require user acknowledgment by using the “-GOFORIT NN” option.
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 and 30: -29- compare EPI images from before
Page 31 and 32: -31-• 3dDeconvolve command Par
Page 33: -33-• 3dDeconvolve command Par
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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