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Transcript of NA-MIC National Alliance for Medical Image Computing fMRI Data Analysis Sonia Pujol, Ph.D. Wendy...
![Page 1: NA-MIC National Alliance for Medical Image Computing fMRI Data Analysis Sonia Pujol, Ph.D. Wendy Plesniak, Ph.D. Randy Gollub, M.D.,](https://reader036.fdocuments.net/reader036/viewer/2022062318/5514ece9550346b0478b5cbb/html5/thumbnails/1.jpg)
NA-MICNational Alliance for Medical Image Computing http://na-mic.org
fMRI Data Analysis
Sonia Pujol, Ph.D.
Wendy Plesniak, Ph.D.
Randy Gollub, M.D., Ph.D.
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National Alliance for Medical Image Computing http://na-mic.org
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
Brain Imaging Laboratory, Dartmouth Medical School
Surgical Planning Lab, Harvard Medical SchoolSandy Wells, Steve Pieper, Cindy Wible, Haiying Liu, Carsten Richter
Acknowledgments
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National Alliance for Medical Image Computing http://na-mic.org
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.
Disclaimer
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National Alliance for Medical Image Computing http://na-mic.org
Guiding you step by step through the process of using the fMRIEngine to analyze fMRI data and visualize results within Slicer. A sensory motor paradigm was used for the tutorial dataset.
Goal of the tutorial
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National Alliance for Medical Image Computing http://na-mic.org
The fMRIEngine is:
• An open-source package for analyzing and visualizing brain activations supporting the file formats DICOM, ANALYZE, and NIfTI.
• A developing framework for a suite of activation detection algorithms and inference engines; currently it provides a linear modeling detector.
• A research prototype: the full save/reload functionalities are under development. There are no capabilities at this time to do the fMRI pre-processing steps.
fMRI engine Module
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National Alliance for Medical Image Computing http://na-mic.org
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:Workshops:User_Training_101
Prerequisites
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National Alliance for Medical Image Computing http://na-mic.org
• This tutorial guides you through a full fMRI analysis of a real fMRI timeseries to get users familiar with the interface and workflow.
• You have the option of using either • a full-dataset (90 time pts) fMRI-long-dataset.zip for which
your computer must have adequate processing speed and RAM (we recommend at least 3GB) or
• a truncated portion (30 time pts) fMRI-short-dataset.zip of the full dataset, that requires at least 1GB RAM.
• The short dataset contains the first 30 time points of the full dataset.
Please use the appropriate dataset for your computer.
Computer Resources
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National Alliance for Medical Image Computing http://na-mic.org
The fMRI tutorial dataset is composed of
Structural scans .………….(anatomical3T.img)
Functional scans………….(functionalxx.img)
www.na-mic.org/Wiki/index.php/Slicer:Workshops:User_Training_101
Tutorial datasets
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National Alliance for Medical Image Computing http://na-mic.org
fMRI Data pre-processing (SPM)
c
Realignment
Motion Correction
Normalization to MNI
Smoothing
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National Alliance for Medical Image Computing http://na-mic.org
Structural (MPRAGE): ANALYZE format135 slices1.0 mm x 1.0 mm x 1.0 mm voxelsNormalized to MNI
Pre-processed Functional (EPI): NIfTI format68 slices2.0 mm x 2.0 mm x 2.0 mm voxelsRepetition Time TR = 2s
Data description
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National Alliance for Medical Image Computing http://na-mic.org
Paradigm description• Finger sequencing fMRI task to elicit activation in
the hand regions of the primary sensory motor cortex
• Block design motor paradigm
• Subject touches thumb to fingers sequentially within block (thumb touches first through fourth finger)
• Subject alternates left and right hand
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National Alliance for Medical Image Computing http://na-mic.org
Paradigm design
0
TRs
10 3020 40 50 60 70 80 90
Cycle 1 Cycle 2 Cycle 3
Three cycles
rest | right hand | left hand
right leftrest right leftrest right leftrest
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National Alliance for Medical Image Computing http://na-mic.org
fMRI Engine compatibilities
SPM fMRI pre-processing
FSL fMRI pre-processing
3DSlicer fMRI full analysis and visualization
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National Alliance for Medical Image Computing http://na-mic.org
fMRI Engine compatibilities
SPM fMRI full analysis
FSL fMRI full analysis
3DSlicer visualization and modeling
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National Alliance for Medical Image Computing http://na-mic.org
Load 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
fMRIEngine workflow
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National Alliance for Medical Image Computing http://na-mic.org
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
Overview
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National Alliance for Medical Image Computing http://na-mic.org
Loading the structural dataset
Click on Add Volume in the main menu
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National Alliance for Medical Image Computing http://na-mic.org
Loading the structural dataset
Click on Browse, select the file Anatomical3T.hdr in the directory/structural.
The anatomical volume in the short and long datasets are identical.
Select the reader Generic Reader in the Props Panel of the module Volumes.
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National Alliance for Medical Image Computing http://na-mic.org
Loading the structural dataset
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National Alliance for Medical Image Computing http://na-mic.org
Select Modules in the main menu
Select ApplicationfMRIEngine
fMRI Engine
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National Alliance for Medical Image Computing http://na-mic.org
Load Image Sequence
Pick Sequence Load tab
Click on Browse and select the file functional01.hdr from either dataset. Select Load Multiple Files
Enter the sequence name testFunctional and click on Apply.
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National Alliance for Medical Image Computing http://na-mic.org
Load Image Sequence
Slicer displays the load status of the 30 (short dataset) or 90 (long dataset) functional volumes.
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National Alliance for Medical Image Computing http://na-mic.org
Load Image Sequence
Slicer loads the functional volumes in the Viewer.
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National Alliance for Medical Image Computing http://na-mic.org
Set Image Display
Click on the module Volumes, and select the panel Display
Adjust Win and Lev to get best display of image data
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National Alliance for Medical Image Computing http://na-mic.org
Set Image Display
Slicer updates the Window and Level settings.
Click on the V button to display the axial slice in the Viewer.
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National Alliance for Medical Image Computing http://na-mic.org
Set Image Display
Click on the letter I in the control window to display the Inferior view.
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National Alliance for Medical Image Computing http://na-mic.org
Set Image Display
Adjust the low threshold Lo to mask out background
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National Alliance for Medical Image Computing http://na-mic.org
Set Image Display
The display settings apply to currently viewed image in the sequence only
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National Alliance for Medical Image Computing http://na-mic.org
Set Sequence Display
Click on Set Window/Level/Thresholds to apply to all volumes in the sequence
Visually inspect sequence using the Volume index to check for intensities aberrations
Click on fMRIEngine, select the panel Sequence, and pick the tab Select
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National Alliance for Medical Image Computing http://na-mic.org
Inspect Image Display
Slicer displays the volumes of the sequence.
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National Alliance for Medical Image Computing http://na-mic.org
Select Image Sequence
Specify the number of runs = 1, select the sequence testFunctional
Click on Add to assign the sequence to run 1
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National Alliance for Medical Image Computing http://na-mic.org
Select Image Sequence
Slicer assigns the sequence to run 1
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National Alliance for Medical Image Computing http://na-mic.org
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
Overview
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National Alliance for Medical Image Computing http://na-mic.org
Stimulus schedule
Pick Set Up Tab in the fMRIEngine and choose the Linear Modeling detector
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National Alliance for Medical Image Computing http://na-mic.org
Linear Modeling
The General Linear Modeling is a class of statistical tests assuming that the experimental data are composed of the linear combination of different model factors, along with uncorrelated noise
Y = BX + e
B = set of experimental parameters
Y = Observed data
X = Design Matrix e = noise
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National Alliance for Medical Image Computing http://na-mic.org
Stimulus schedule
Select the design type Blocked
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National Alliance for Medical Image Computing http://na-mic.org
Paradigm timing parameters
• Repetition Time TR = 2s • Durations: 10 TRs in all epochs• Onsets (in TRs):
Rest: 0 30 60
Right: 10 40 70
Left : 20 50 80
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National Alliance for Medical Image Computing http://na-mic.org
Stimulus schedule
Enter the characteristics of the runTR = 2 and Start Volume = 0 (ordinal number)
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National Alliance for Medical Image Computing http://na-mic.org
Stimulus schedule Enter the schedule for the first condition
Short dataset: Name = right Onset = 10 Durations = 10
Long dataset:Name = right Onset = 10 40 70 Durations = 10 10 10
Click on OK to add this conditionto the list of defined conditions
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National Alliance for Medical Image Computing http://na-mic.org
Stimulus schedule Enter the schedule for the second condition
Short dataset: Name = left Onset = 20 Durations = 10
Long dataset:Name = leftOnset = 20 50 80 Durations = 10 10 10
Click on OK to add this conditionto the list of defined conditions
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National Alliance for Medical Image Computing http://na-mic.org
Stimulus schedule
Scroll down in the Set-up panel to see the list of defined conditions
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Editing the Stimulus schedule
The list of specified conditions appears in the left panel
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National Alliance for Medical Image Computing http://na-mic.org
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
Overview
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National Alliance for Medical Image Computing http://na-mic.org
Model a Condition
Select Specify Modeling
Click on Model all conditions identically
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National Alliance for Medical Image Computing http://na-mic.org
Model a Condition
Select Condition: all Waveform: BoxCar
Click on the question mark next to Waveform for detailed description of the modeling option.
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National Alliance for Medical Image Computing http://na-mic.org
Model a Condition
Slicer displays a detailed description of the Stimulus function.
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National Alliance for Medical Image Computing http://na-mic.org
Model a Condition
Select - Convolution: HRF(Hemodynamic Response Function) - Derivatives: none
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National Alliance for Medical Image Computing http://na-mic.org
Nuisance Signal Modeling
On the subpanel Nuisance signal modeling, select
Trend model: Discrete Cosine Cutoff period: default
Click on use default cutoff
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National Alliance for Medical Image Computing http://na-mic.org
Nuisance Signal Modeling
Scroll down in the Set Up panel and click on add to model
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National Alliance for Medical Image Computing http://na-mic.org
Nuisance Signal Modeling
The list of explanatory variables (EV) appears in the left panel, including the baseline that is automatically added. The string are Slicer specific representation of the modeling.
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View Design Matrix
Click View Design to display the design matrix
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View Design Matrix
Short dataset Long dataset
A window displaying the model design appears.
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National Alliance for Medical Image Computing http://na-mic.org
Design Matrix
v1 = left modeled conditionv2 = right modeled conditionv3 = baseline v4,v5,v6 = low frequency noise
Move the mouse from left to right over the columns of the matrix to display the characteristics of the modeled conditions.
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Design Matrix
White positive signal intensity 1Mid-Grey null intensity 0Black negative intensity - 1
Observe the different values of the signal intensity in the matrix.
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Design Matrix
tp
t
Modeled Signal
Y(tp) = b1 v1(tp) + b2 v2(tp) +b3 v3 (tp) + b4 v4(tp) + b5 v5(tp) + b6 v6(tp)
Y(t)
t
Each column represents the contribution from each condition we might see in a voxel time course.
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Design Matrix
Move the mouse up and down to browse the different volumes associated with the time points.
tp
t
Y(t)
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Estimation
Select Specify Estimation to estimate B and e at every voxel:
Y = BX + e
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Estimating model parameters
Select run1 and click on Fit Model
The Estimation panel appears
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Estimating model parameters
Slicer shows the progress of model estimation
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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
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Specify Contrasts
In the SetUp panel, select Specify Contrasts
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Specify Contrasts
The Panel for the contrasts appears
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Specify Contrasts
Choose the contrast type t-test
Enter the contrast name myContrast, and the Volume Name R-L_activation
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Contrast Vector
• Encoding of the effect that you want to test
• A contrast component per column in the design matrix ( trailing zeros may be omitted)1 0 0 0 0 0 test for whether there is any effect for the right hand 1 -1 0 0 0 0 statistically contrast the effect for the right and left hand
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Specify Contrasts
Specify the contrast vector 1 –1 0 0 (enter a space between the values)
Click OK to add this contrast to a list of defined contrasts
Select the statistical test t-test
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Specify Contrasts
The resulting contrast named myContrast-R-L_activationappears in the list of specified contrasts.
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Check contrasts & model
Click on View Design to display the Design matrix
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Design Matrix
Short dataset Long dataset
Check that the contrast and model are correct.
A window displaying the design matrix and contrast vector appears.
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Perform activation detection
Click on the tab Detectand select the contrast myContrast-R-L_activation
Click on Compute to compute the statistical map of activation (t-test)
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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
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Select the activation volume
Select the resulting activation volume (t-map)myContrast-R-L_activation
Click on Select
Click on the View Tab
Select the subpanel Choose
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Threshold
Click on the Thrshold Tab
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Threshold
Slicer indicates the degree of freedom (DoF): Nvol-1
Short dataset DoF=29
Long dataset DoF=89
Specify the p-Value threshold 0.001 and hit Enter
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Null hypothesis
• H0: there is no difference between the right hand condition and left hand condition on the fMRI signal. This is tested at each voxel.
• If the resulting probability is lower than the experiment’s alpha value (p <0.001), the null hypothesis can be rejected.
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Threshold
Slicer calculates the corresponding threshold t Stat
Short dataset
t Stat = 3.7
Long dataset
t Stat = 3.4
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Activation mapSlicer displays the activation map overlaid on the fMRI images
Short dataset Long dataset
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fMRI color palette
Select the panel Displayand set the Active Volume to be the activation volume myContrast-R-L_activationMap
Click on the module Volumes
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fMRI color paletteAdjust the Window and Level of the color palette for the volume myContrast-R-L_activationMap
Short dataset
Long dataset
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fMRI color palette
-MAX +MAX
No statistical significance
Positive activation
Negative activation
-MAX +MAX
No statistical significance
Positive activation
Negative activation
Short dataset
Long dataset
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Activation mapSlicer displays the activation map overlaid on the fMRI images
Short dataset
Long dataset
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VisualizeLeft click on Bg in the 2D anatomical viewers to display the volume anatomical 3T in background
Short dataset
Long dataset
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Visualize
Short dataset
Long dataset
Slicer displays the activation map superimposed on the anatomical images.
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Inspect
Pick the tab Plot and select the condition = right
Select Timecourse plot option
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Inspect
Mouse over labelled area in Slice Window and left click on the pixel R = -40 A = 0 S = 20, which is highly significant in the activation map. The left-hemisphere of the subject is shown on the right side of the image, in radiological convention.
Short dataset Long dataset
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Voxel TimecourseSlicer displays the voxel’s actual timecourse (response) plotted with the modeled condition (right hand) for the selected voxel.
Short dataset Long dataset
The graphs show a good correlation between the observed BOLD signal Y(t) and the model.
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InspectMouse over labelled area in Slice Window and left click on the pixel R = 40 A = 0 S = 20, which is highly significant in the opposite direction.
Short dataset Long dataset
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National Alliance for Medical Image Computing http://na-mic.org
Voxel TimecourseSlicer displays the voxel’s timecourse plotted with the modeled condition for the selected voxel
Short dataset Long dataset
If we were plotting the left hand condition, what correlation would be observed?
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Contralateral side vs Ipsilateral side (short dataset)
During the right hand condition, the observed signal decreases in the ipsilateral side and increases on the contralateral side.
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Contralateral side vs Ipsilateral side (long dataset)
During the right hand condition, the observed signal decreases in the ipsilateral side and increases on the contralateral side.
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Inspect
Select Peristimulus plot option and click on the voxel (-40,0,20) in the positive activation region
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Voxel Peristimulus PlotSlicer displays a plot of the mean time course values of theselected voxel in the positive activation region duringdifferent blocks.
Short dataset Long dataset
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Inspect
Select Peristimulus histogram option and click on the voxel in the negative activation region (40,0,20)
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Voxel Peristimulus PlotSlicer displays a plot of the mean time course values of theselected voxel in the negative activation region duringdifferent blocks.
Short dataset Long dataset
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Activation-based region of interest
Select the ROI panel and RegionMap tabChoose New Activation from Label map
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Activation-based region of interest
Click Create label map from activation, and wait while activation “blobs” are labelled
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Activation-based region of interest
The label map is shown in Foreground, and the activation map is shown in Background.
Short dataset Long dataset
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Region Statistics
Select the subtab StatsSelect one or multiple regions in the left hemisphere to include in analysis by clicking in Slice Window.
Select the condition right.
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Region Statistics
Short dataset Long dataset
The selected regions appear in green.
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Region Statistics
Click Show stats to display the statistics for the selected regions
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Region Statistics
Slicer displays the statistics for the selected region(s)
Short dataset Long dataset
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Region Timecourse
Select Timecourse plot option and click on Plot time series for this region.
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Region Timecourse
Slicer displays the region timecourse plot
Short dataset Long dataset
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Region Peristimulus Plot
Select Peristimulus plot and click Plot time series for this region.
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Region Peristimulus PlotSlicer displays the Region Peristimulus Plot
Short dataset Long dataset
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3D Visualization
Click on Clear selections and display the structural volume anatomical3T in the background (Bg) and the activation map myContrast-R-L_activation in the foreground (Fg).
Display three anatomical slices in the 3D Viewer.
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3D Visualization
Short dataset Long dataset
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3D Visualization
Fade in the activation volume for a good view of combined data
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3D Visualization
Short dataset Long dataset
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National Alliance for Medical Image Computing http://na-mic.org
Conclusion
• Analysis and visualization of fMRI data
• Framework activation detection algorithms and inference engines
• Open-Source environment