NA-MICNational Alliance for Medical Image Computing http://na-mic.org

fMRI Data Analysis in Slicer(short dataset tutorial)

Steve Pieper

Haiying Liu

Wendy Plesniak

Sandy Wells

Cindy Wible

Carsten Richter

National Alliance for Medical Image Computing http://na-mic.org

Acknowledgments

National Alliance for Medical Image ComputingNIH U54EB005149

Neuroimage Analysis Center NIH P41RR013218

FIRST Biomedical Informatics Research Network NCRR/NIH 5 MOI RR 000827

Harvard Center for Neurodegeneration and Repair

Data was acquired on a Philips Achieva 3T scanner. Courtesy of the Brain Imaging Laboratory, Dartmouth Medical School, a participating NAMIC site.

National Alliance for Medical Image Computing http://na-mic.org

Disclaimer

It is the responsibility of the user of 3DSlicer

to comply with both the terms of the license

and with the applicable laws, regulations

and rules.

National Alliance for Medical Image Computing http://na-mic.org

Goal of the tutorial

Guiding you step by step through the process of using the fMRIEngine to analyze fMRI data and visualize results within Slicer.

National Alliance for Medical Image Computing http://na-mic.org

Prerequisites

This tutorial assumes that you have already completed Slicer Basics:

• Loading and Viewing Data (Slicer Training 1)• Saving Data ( Slicer Training 7)

Supporting material: www.na-mic.org/Wiki/index.php/Slicer:Slicer2.6_Getting_Started

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Module current status

The fMRIEngine is:

• A package for analyzing and visualizing brain activations in Slicer’s multi-modality environment.

• a developing framework for a suite of activation detection algorithms and inference engines; currently it provides a basic linear modeling detector only.

• a prototype under development; it does not yet have full save/load functionality or full compatibility with other fMRI packages.

National Alliance for Medical Image Computing http://na-mic.org

Computer Resources• The short-dataset tutorial teaches you how the

module works; it uses a truncated fMRI timeseries to get users quickly familiar with the interface and workflow ( we recommend at least 1GB RAM).

• The full-dataset tutorial guides you through a full fMRI analysis. For this part, your computer must have adequate processing speed and RAM (we recommend at least 3GB).

Please use the appropriate tutorial and dataset for you.

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Tutorial datasetsDownload: fMRIEngineTutorial.zip www.na-mic.org/Wiki/index.php/Slicer:Workshops:User_Training_101

Short dataset: functional scans (file format Analyze):

functional01.hdr, functional01.img … functional90.hdr, functional90.img structural scan (file format NifTI): anatomical3T.hdr, anatomical3T.img

Full dataset: functional scans (file format Analyze): functional01.hdr, functional01.img … functional90.hdr, functional90.img structural scan (file format NifTI): anatomical3T.hdr, anatomical3T.img

National Alliance for Medical Image Computing http://na-mic.org

Tutorial datasets

What loaded data looks like:

• Functional scans………….(functional01.img)

• Structural scan ..………….(anatomical3T.img)

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Data description

Functional (EPI): 45 slices, 90 scans; 3.75 x 3.75 x 3.75 mm3 voxels (64 x 64 matrix with 240mm FOV). Transverse acquisition.

Preprocessing: realigned, motion corrected, normalized to MNI, smoothed.

Structural (MPRAGE): 170 sagittal slices; 1x1x1.2 mm3 voxels; normalized to MNI.

National Alliance for Medical Image Computing http://na-mic.org

ParadigmFinger sequencing fMRI task• Blocked motor paradigm• Subject alternates simple left and right thumb to finger sequencing

(always same thumb to 1,2,3,4 finger order within block).• Three cycles each of rest, right hand and left hand.

TR = 2 secondsDurations: 10 TRs in all epochsOnsets (in TRs):

Rest: 0 30 60 Right : 10 40 70 Left : 20 50 80

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Slicer’s fMRIEngine

National Alliance for Medical Image Computing http://na-mic.org

fMRIEngine workflowLoad preprocessed functional data

Describe paradigm and stimulus schedule

Specify linear modeling & estimate model parameters

Define contrasts and compute parametric map

Statistical inference

inspect data & combine with other analyses

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Overview

Part 1: Loading and Previewing Data

Part 2: Describing stimulus schedule

Part 3: Linear modeling & estimation

Part 4: Contrasts & computing SPMs

Part 5: Inference & inspection

National Alliance for Medical Image Computing http://na-mic.org

Load Image Sequence

1: Pick Sequence->Load tab

7: Click Apply

2: Click File Browse

6: Input a sequence name

5: Input a file filter

4: Click Load Multiple Files

3: Pick any file in thefunctional directory

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Set Image Display

11: operations will apply to currently viewed image in the sequence only…

8: Go to Volumes->Display

9: Adjust Win and Lev to get best displayof image data

10: Adjust Lo threshold to mask out non-brain

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Select Image Sequence

13: Pick Select Tab

14: Specify number of runs = 1

15: Click Add toassign sequence to run 1

16: Click Set Window/Level/Thresholdsto apply to all volumes inthe sequence

12: Go back to fMRIEngine

Visually inspect sequence

National Alliance for Medical Image Computing http://na-mic.org

Overview

Part 1: Loading and Previewing Data

Part 2: Describing stimulus schedule

Part 3: Linear modeling & estimation

Part 4: Contrasts & computing SPMs

Part 5: Inference & inspection

National Alliance for Medical Image Computing http://na-mic.org

Stimulus schedule

16: Pick Set Up Tab and choosethe Linear Modeling detector

17: Choose Paradigm Design

18: Enter schedule for first condition(name = right)

19: Click OK to add this conditionto the list of defined conditions

}

detection technique

linear modeling step

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Stimulus schedule

20: Enter schedule for nextcondition (Name = left)

21: Click OK to add this conditionto the list too…

}

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Stimulus schedule

22: To change the schedule forany condition: select condition from box, click Edit, make changes,and click OK again to re-assign it.

23: To delete any defined condition: select it and clickDelete

Making modifications…

After clicking OK, each specified condition for Run 1 is listed as shown below, labeled “r1:condition_name”

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Overview

Part 1: Loading and Previewing Data

Part 2: Describing stimulus schedule

Part 3: Linear modeling & estimation

Part 4: Contrasts & computing SPMs

Part 5: Inference & inspection

National Alliance for Medical Image Computing http://na-mic.org

Model Signal

24: Choose the Signal Modeling step

25: Click to model both conditions identically

28: model drift with Discrete Cosine set

27: convolve with HRF

30: Add to Model26: choose boxcar waveform

29: Choose to use default cutoff

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View Design Matrix

31: Click View Design

32: The model design matrix appears in a separate window(this may take some time)

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Estimating model parameters

33: Choose the estimation step

34: Select run1 and click Fit Model

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Overview

Part 1: Loading and Previewing Data

Part 2: Describing stimulus schedule

Part 3: Linear modeling & estimation

Part 4: Contrasts & computing SPMs

Part 5: Inference & inspection

National Alliance for Medical Image Computing http://na-mic.org

Specify Contrasts

35: Choose Contrasts step

36: Specify Name (c1)37: Name activation volume38: Specify contrast vector (1 –1 0 0)39: Click OK to add this contrast to a list of defined contrasts

40: Contrast name appears here

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Check contrasts & model

41: Click View Design

42: Verify contrast is correct

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Perform activation detection

43: Select Detect tab

44: Select contrast to compute for

45: Click Compute

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Overview

Part 1: Loading and Previewing Data

Part 2: Describing stimulus schedule

Part 3: Linear modeling & estimation

Part 4: Contrasts & computing SPMs

Part 5: Inference & inspection

National Alliance for Medical Image Computing http://na-mic.org

Threshold, visualize, inspect

46: Pick View Tab

47: Select activation volume

48: Click Select

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Threshold, visualize, inspect

49: Pick Threshold Tab

50: Select uncorrected p-value

51: Specify p-value threshold = 0.001

Activations appear in the foreground

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Threshold, visualize, inspect

Display anatomical image in background

Color map code:Strong negative activation

Below p-value threshold (not rendered)

Weak negative activation

Weak positive activation

Strong positive activation

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Threshold, visualize, inspect

52: Pick Plot tab

53: Select condition = right

54: Select Timecourse plot option

Plotting…

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Threshold, visualize, inspect

55: Mouse over labeled voxelin Slice Window and click…

view voxel’s timecourse plotted with the modeled condition

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Threshold, visualize, inspect56: Select Peristimulus histogram option and click a voxel labeled as active

57: Click any curve on the peristimulus plot for details

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Threshold, visualize, inspect

58: Select ROI tab

59: Choose New from label map pulldown menu…

Activation-based Regions of Interest…

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Threshold, visualize, inspect

60: Click Create label map…

61: Wait short while while activation “blobs” are labeled

62: Default blob view:Label map shown in FG; activation map shown in BG.

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Threshold, visualize, inspect

63: Select Stats tab

64: Select one or multiple regions to include in analysis by clicking in Slice Window. Selected regions display green.

65: (clear all selections and start over if you make a mistake)

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Threshold, visualize, inspect

66: Click Show stats

View region statistics

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Threshold, visualize, inspect

67: Select Timecourse plot option

68: Click Plot time series for this region

View region statistics

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Threshold, visualize, inspect

69: Select Peristimulus histogram option

70: Click Plot time series for this region

View region statistics

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Threshold, visualize, inspect

72: Fade in the activation volume a little more for agood view of combined data

71: Clear selections.

Then display anatomical image in BG and activation in FG.

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Threshold, visualize, inspect

Adjusting colormaps for visualization…

73: Navigate to Volumes->Display

74: Set the Active Volume to be the activation volume

75: Adjust Window and Level to shift the color map hue range

Recall color map code:Strong negative activation

Below p-value threshold (not rendered)

Weak negative activation

Weak positive activation

Strong positive activation

National Alliance for Medical Image Computing http://na-mic.org

Threshold, visualize, inspect

To view only positive activations…

76: Choose Palette to be fMRIPosActive

75: Adjust Window and Level to shift the color map hue range

Color map code:

Below p-value threshold (not rendered)

Weak positive activation

Strong positive activation

National Alliance for Medical Image Computing http://na-mic.org

Features to come…

Features that will be developed within the fMRIEngine:

• Load/Save functionality for modeling and analysis

• Ability to apply prior models

• Ability to perform other statistical tests

• Other techniques for performing activation detection

• Advanced visualization

• …