dset 1 1 1 stats.sb04.betas+tlrc

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-6-• Back to afni_proc.py : For our class example, we can take a look at the afni_proc.py command we’vewritten up already and saved as an executable script calleds1.afni_proc.block (perhaps viewing in a different terminal window)cd AFNI_data4gedit s1.afni_proc.block• note: gedit is a text editor (can also use nedit, emacs or vi)afni_proc.py \-subj_id sb23.blk \-<strong>dset</strong>s sb23/epi_r??+orig.HEAD \-copy_anat sb23/sb23_mpra+orig \-tcat_remove_first_trs 3 \-volreg_align_to last \-regress_make_ideal_sum sum_ideal.1D \-regress_stim_times sb23/stim_files/blk_times.*.1D \-regress_stim_labels tneg tpos tneu eneg epos \eneu fneg fpos fneu \-regress_basis 'BLOCK(30,1)' \-regress_opts_3dD \-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
-7- LinebyLine Explanation of s1.afni_proc.block command:-subj_id: Specify a subject ID name for the processing script that will becreated by executing the s1.afni_proc.block script. The ID inthis example is sb23.blk-<strong>dset</strong>s : Specify the name of the time series datasets that will be analyzed, aswell as the directory path in which they reside. Here, they are calledepi_r03+orig .. epi_r11+orig, residing in directory sb23/(note that .HEAD is required for wildcard matching)-copy_anat: This option will take the anatomical dataset sb23_mpra+origthat currently resides in sb23/ and copy it into the results directory(the results directory will be created once the processing script hasbeen run)tcat_remove_first_trs: This option removes ‘x’ number of timepointsfrom the beginning of each time series run. Here, we have chosento remove the first 3 timepoints from each run-volreg_align_to: This volume registration option asks the user to choosewhich volume will be the base by which all other volumes in the timeseries runs are aligned. Here we have chosen the last volumefrom the last epi run (run 9)-regress_make_ideal_sum: Sums the ideal response curves from theregressors and saves as a 1D file, e.g., sum_ideal.1D-regress_stim_times: Specifies the name and location of the stimulustiming files for our experiment. In this example, they aresb23/stim_files/blk_times.*.1D
Page 1 and 2: -1-AFNI Soup to Nuts:How to Analyze
Page 3 and 4: -3-• The Experiment : Cognitive T
Page 5: -5-• Analysis Steps: Part I: Proc
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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