Data Processing & Analysis of
Resting-State fMRI
Chao-Gan YAN, Ph.D.
严超赣
ycg.yan@gmail.com
http://rfmri.org
Research Scientist
The Nathan Kline Institute for Psychiatric Research
Research Assistant Professor
Department of Child and Adolescent Psychiatry /
NYU Langone Medical Center Child Study Center, New York University
Outline
• Overview
• Data Preparation
• Preprocessing
• R-fMRI measures Calculation
• Quality Control
• Statistical Analysis
• Results Viewing
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DPARSF
(Yan and Zang, 2010)
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Data Processing
Assistant for RestingState fMRI
(DPARSF)
Yan and Zang, 2010. Front Syst Neurosci.
http://rfmri.org/DPARSF
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DPABI: a toolbox for
Data Processing &
Analysis of Brain
Imaging
License: GNU GPL
Chao-Gan Yan
Xin-Di Wang
Programmer
Initiator
Programmer
http://rfmri.org/dpabi
http://dpabi.org
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Outline
• Overview
• Data Preparation
• Preprocessing
• R-fMRI measures Calculation
• Quality Control
• Statistical Analysis
• Results Viewing
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Data Organization
ProcessingDemoData.zip
FunRaw
Sub_001
Functional DICOM data
Sub_002
Sub_003
T1Raw
Sub_001
Structural DICOM data
Sub_002
Sub_003
http://rfmri.org/DemoData
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Data Organization
ProcessingDemoData.zip
FunImg
Sub_001
Functional NIfTI data
(.nii.gz., .nii or .img)
Sub_002
Sub_003
T1Img
Sub_001
Structural NIfTI data
(.nii.gz., .nii or .img)
Sub_002
Sub_003
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Outline
• Overview
• Data Preparation
• Preprocessing
• R-fMRI measures Calculation
• Quality Control
• Statistical Analysis
• Results Viewing
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Preprocessing
Working Dir where
stored Starting
Directory (e.g.,
FunRaw)
Detected
participants
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Preprocessing
Detected participants
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Preprocessing
Number of time points
(if 0, detect
automatically)
TR
(if 0, detect from
NIfTI header)
Template Parameters
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Resting State
fMRI Data
Processing
Template Parameters
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What’s new?
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Reorient and
Quality control
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Automask
generation
For checking EPI coverage
and generating group mask
FunImgAR/Sub_001
Masks/AutoMasks/
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Brain extraction
(Skullstrip)
For better coregistration
For Linux and Mac:
Need to install FSL.
For Windows:
Thanks to Chris
Rorden's compiled
version of bet in
MRIcroN, our modified
version can work on
NIfTI images directly.
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Bet & Coregistration
T1ImgCoreg/Sub_001
Segment
bet
RealignParameter/Sub_001/mean*.nii
T1Img/Sub_001
Apply
Coregister
T1ImgBet/Sub_001
RealignParameter/Sub_001/Bet_mean*.nii
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Nuisance
regression
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Nuisance
Regression
 Mask based on segmentation or SPM apriori
 CompCor or mean [note: for CompCor, detrend (demean) and variance
normalization will be applied before PCA, according to Behzadi et al., 2007]
 Global Signal based on Automask
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Outline
• Overview
• Data Preparation
• Preprocessing
• R-fMRI measures Calculation
• Quality Control
• Statistical Analysis
• Results Viewing
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R-fMRI
measures
Calculation
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Outline
• Overview
• Data Preparation
• Preprocessing
• R-fMRI measures Calculation
• Quality Control
• Statistical Analysis
• Results Viewing
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Quality Control
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Quality Control
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Quality Control
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Quality Control
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Quality Control
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Quality Control
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Quality Control
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Quality Control
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Quality Control
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Quality Control
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Quality Control
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Quality Control
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Quality Control
This mask is very important for group statistical analysis!!!
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Quality Control
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Quality Control
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Quality Control
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Quality Control
 Using the visual inspection step within DPARSF, subjects showing severe head
motion in the T1 image and subjects showing extremely poor coverage in the
functional images, as well as subjects showing bad registration were excluded
 Subjects with overlap with the group mask (voxels present at least 90% of the
participants) less than 2*SD under the group mean overlap (threshold: 92.2%) were
excluded
 Subjects with motion (Mean FD Jenkinson greater than 2*SD above the group mean
motion (threshold: 0.192) were excluded
Yan et al., 2013, Neuroimage
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Outline
• Overview
• Data Preparation
• Preprocessing
• R-fMRI measures Calculation
• Quality Control
• Statistical Analysis
• Results Viewing
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Statistical Analysis
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Statistical Analysis
{DPABI_Dir}/StatisticalAnalysis/y_GroupAnalysis_Image.m
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Attention!!!
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Statistical Analysis
{DPABI_Dir}/StatisticalAnalysis/y_GroupAnalysis_Image.m
Smoothness estimation based on the 4D residual is built in this function!!!
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Statistical Analysis
http://rfmri.org/DemoData
{Download}/ProcessingDemoData/StatisticalDemo/AD_MCI_NC/
ALFF: AD – NC Two Sample T Test:
• Applied smooth kernel in preprocessing: [4 4 4]
• Smooth kernel estimated on 4D residual: [6.77 6.88 6.71]
• Smooth kernel estimated on statistical image (T to Z, as in easythresh): [6.90 7.33 6.94]
ReHo: AD – NC Two Sample T Test:
• Applied smooth kernel in preprocessing: [4 4 4]
• Smooth kernel estimated on 4D residual: [8.10 8.50 7.93]
• Smooth kernel estimated on statistical image (T to Z, as in easythresh): [8.33 8.94 8.24]
Thus, only using smooth kernel applied in preprocessing is NOT sufficient!!!
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Outline
• Overview
• Data Preparation
• Preprocessing
• R-fMRI measures Calculation
• Quality Control
• Statistical Analysis
• Results Viewing
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Voxel Z > 2.3, Cluster P < 0.05, Two One-Tailed Corrections:
equivalent to
Voxel P < 0.0214, Cluster P < 0.1, Two Tailed.
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Further Help
Further questions:
http://rfmri.org/dpabi
http://dpabi.org
The R-fMRI Network
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Further Help
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Send emails only to rfmri.org@gmail.com:
1) sending new email means you are
posting your personal blogs, 2) replying
email means you are posting comments to
that topic/blog, 3) then all the other RfMRI nodes will receive email updates of
your posts.
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Xin-Di Wang
Yu-Feng Zang
Programmer
Consultant
Acknowledgments
Nathan Kline Institute
Charles Schroeder
Stan Colcombe
Gary Linn
Mark Klinger
NYU Child Study Center
F. Xavier Castellanos
Adriana Di Martino
Clare Kelly
Child Mind Institute
Michael P. Milham
R. Cameron Craddock
Zhen Yang
Hangzhou Normal University
Yu-Feng Zang
Beijing Normal University
Yong He
Fudan University
Tian-Ming Qiu
Chinese Academy of Sciences
Xi-Nian Zuo
Princeton University
Han Liu
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F. Xavier Castellanos
NKI/NYU
Charles E. Schroeder
NKI/Columbia
David A. Leopold
NIMH
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Thanks for your attention!
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