dset 1 1 1 stats.sb04.betas+tlrc

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-12-• STEP 1 (tshift): Check for possible “outliers” in each of the 9 time seriesdatasets using 3dToutcount . Then perform temporal alignment using3dTshift. An outlier is usually seen as an isolated spike in the data, which may be due to anumber of factors, such as subject head motion or scanner irregularities. The outlier is not a true signal that results from presentation of a stimulus event, butrather, an artifact from something else it is noise.foreach run (01 02 03 04 05 06 07 08 09)3dToutcount -automask pb00.$subj.r$run.tcat+orig \> outcount_r$run.1Dend How does this program work? For each time series, the trend and Median AbsoluteDeviation are calculated. Points far away from the trend are considered outliers."far away" is defined as at least 5.219*MAD (for a time series of 64 TRs)• see 3dToutcount -help for specifics -automask: does the outlier check only on voxels within the brain and ignoresbackground voxels (which are detected by the program because of their smallerintensity values) > : redirects output to the text file outcount_r01.1D (for example), instead ofsending it to the terminal window.
-13- Subject sb23’s outlier files:outcount_r01.1Doutcount_r02.1D&outcount_r09.1D Use AFNI 1dplot to display any one of ED’s outlier files. For example:1dplot outcount_r08.1DNote: “1D” is used to identify anumerical text file. In this case, eachfile consists a column of 64 numbers(b/c of 64 time points).Number of‘outlier’ voxels,per TROutliers?Inspect the data.High intensity valuesin the beginning areusually due toscanner attemptingto reach steadystate.Time
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: -11-• STEP 0 (tcat): Apply 3dTcat
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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