Data Processing & Analysis of Resting-State fMRI

Slides:



Advertisements
Similar presentations
1 Usage of REST-GCA Zang Zhenxiang School of Science, Beijing jiaotong university State Key Laboratory of Cognitive Neuroscience and Learning, Beijing.
Advertisements

1 Data Processing of Resting-State fMRI (Part 2) YAN Chao-Gan Ph. D. State Key Laboratory of Cognitive Neuroscience and Learning, Beijing.
Data Processing of Resting-State fMRI (Part 2)
Data Processing of Resting-State fMRI (Part 1)
Data Processing of Resting-State fMRI (Part 3): DPARSF Advanced Edition YAN Chao-Gan 严超赣 Ph. D. State Key Laboratory of Cognitive Neuroscience.
Buttons in SPM5 Carolyn McGettigan & Alice Grogan Methods for Dummies 5 th April 2006.
Concepts of SPM data analysis Marieke Schölvinck.
DPARSF Advanced Edition V2.2
Gordon Wright & Marie de Guzman 15 December 2010 Co-registration & Spatial Normalisation.
Data Processing & Analysis of Resting-State fMRI (Part II) Chao-Gan YAN, Ph.D. 严超赣 Research Scientist The Nathan Kline.
Quality Assurance NITRC Enhancement Grantee Meeting June 18, 2009 NITRC Enhancement Grantee Meeting June 18, 2009 Susan Whitfield-Gabrieli & Satrajit Ghosh.
Introduction to Functional and Anatomical Brain MRI Research Dr. Henk Cremers Dr. Sarah Keedy 1.
1 DPARSF Advanced Edition V2.2 YAN Chao-Gan 严超赣 Ph. D. Nathan Kline Institute, Child Mind Institute and New York University Child Study.
Quality Control of Diffusion Weighted Images
OverviewOverview Motion correction Smoothing kernel Spatial normalisation Standard template fMRI time-series Statistical Parametric Map General Linear.
SPM5 Tutorial, Part 1 fMRI preprocessing Tiffany Elliott May
Coregistration and Normalisation By Lieke de Boer & Julie Guerin.
Multiple testing Justin Chumbley Laboratory for Social and Neural Systems Research Institute for Empirical Research in Economics University of Zurich With.
07/01/15 MfD 2014 Xin You Tai & Misun Kim
Dissociating the neural processes associated with attentional demands and working memory capacity Gál Viktor Kóbor István Vidnyánszky Zoltán SE-MRKK PPKE-ITK.
Multiple comparison correction Methods & models for fMRI data analysis 29 October 2008 Klaas Enno Stephan Branco Weiss Laboratory (BWL) Institute for Empirical.
FMRI Preprocessing John Ashburner. Contents *Preliminaries *Rigid-Body and Affine Transformations *Optimisation and Objective Functions *Transformations.
Co-registration and Spatial Normalisation
2nd Level Analysis Jennifer Marchant & Tessa Dekker
MNTP Trainee: Georgina Vinyes Junque, Chi Hun Kim Prof. James T. Becker Cyrus Raji, Leonid Teverovskiy, and Robert Tamburo.
2nd level analysis – design matrix, contrasts and inference
From Localization to Connectivity and... Lei Sheu 1/11/2011.
Statistical Models for the Analysis of Brain Connectivity Based on fMRI Data Yoshio Takane McGill University and University of Victoria September 19, 2013.
DTU Medical Visionday May 27, 2009 Generative models for automated brain MRI segmentation Koen Van Leemput Athinoula A. Martinos Center for Biomedical.
1 Hands-On Data Analysis Kate Pirog Revill and Chris Rorden Data from safety training –9 subjects –Finger-tapping task (12s tapping, 12s rest) –188 scans.
OPTIMIZATION OF FUNCTIONAL BRAIN ROIS VIA MAXIMIZATION OF CONSISTENCY OF STRUCTURAL CONNECTIVITY PROFILES Dajiang Zhu Computer Science Department The University.
Coregistration and Spatial Normalisation
FMRI Group Natasha Matthews, Ashley Parks, Destiny Miller, Ziad Safadi, Dana Tudorascu, Julia Sacher. Adviser: Mark Wheeler.
Reduced Local BOLD Correlation in Pre-Frontal Cortex during Deep Sleep
Bayesian Inference and Posterior Probability Maps Guillaume Flandin Wellcome Department of Imaging Neuroscience, University College London, UK SPM Course,
Functional Brain Signal Processing: EEG & fMRI Lesson 15 Kaushik Majumdar Indian Statistical Institute Bangalore Center M.Tech.
Diffusion Tensor Imaging: The Nitty Gritty Brought to you by: Meenal and Erica November 2, 2010.
Multimodal Neuroimaging Training Program
Fig.1. Flowchart Functional network identification via task-based fMRI To identify the working memory network, each participant performed a modified version.
Correlation random fields, brain connectivity, and astrophysics Keith Worsley Arnaud Charil Jason Lerch Francesco Tomaiuolo Department of Mathematics and.
Voxel-based morphometry The methods and the interpretation (SPM based) Harma Meffert Methodology meeting 14 april 2009.
Data Processing of Resting-State fMRI: Principles
National Alliance for Medical Image Computing Core What We Need from Cores 1 & 2 NA-MIC National Alliance for Medical Image Computing.
Conclusions Simulated fMRI phantoms with real motion and realistic susceptibility artifacts have been generated and tested using SPM2. Image distortion.
Spatial Smoothing and Multiple Comparisons Correction for Dummies Alexa Morcom, Matthew Brett Acknowledgements.
SPM Pre-Processing Oli Gearing + Jack Kelly Methods for Dummies
SPM Software & Resources Wellcome Trust Centre for Neuroimaging University College London SPM Course London, October 2008.
A Temporal Filtering Algorithm to Reconstruct Daily Albedo Series Based on GLASS Albedo product Nanfeng Liu 1,2, Qiang Liu 1,2, Lizhao Wang 2, Jianguang.
Methods for Dummies Second level Analysis (for fMRI) Chris Hardy, Alex Fellows Expert: Guillaume Flandin.
Arterial spin labeling
Statistical Analysis An Introduction to MRI Physics and Analysis Michael Jay Schillaci, PhD Monday, April 7 th, 2007.
Dynamic Connectivity: Pitfalls and Promises
1 st level analysis: Design matrix, contrasts, and inference Stephane De Brito & Fiona McNabe.
The general linear model and Statistical Parametric Mapping I: Introduction to the GLM Alexa Morcom and Stefan Kiebel, Rik Henson, Andrew Holmes & J-B.
NA-MIC National Alliance for Medical Image Computing Velocardiofacial Syndrome as a Genetic Model for Schizophrenia Marek Kubicki DBP2,
TMBIC 陳尹華、李佩芳、盧毓文、鄭旭博. Data Raw Sub 1 Anatomical images (T1) Functional images (EPI) Sub 2 (DICOM &) Preprocessing Sub 1 Anatomical images (T1) Functional.
Bayesian Methods Will Penny and Guillaume Flandin Wellcome Department of Imaging Neuroscience, University College London, UK SPM Course, London, May 12.
Variance components Wellcome Dept. of Imaging Neuroscience Institute of Neurology, UCL, London Stefan Kiebel.
Guillaume Flandin Wellcome Trust Centre for Neuroimaging University College London SPM Course Zurich, February 2008 Bayesian Inference.
Am I my connectome? Statistical issues in functional connectomics Brian Caffo, PhD Department of Statistics at National Cheng-Kung University, Taiwan 2015.
ABSTRACT Figure 3. Results from two ANOVAs (HV > 0, MDD > 0) and a flexible factorial design (HV vs. MDD) in the unpleasant > neutral contrast are shown.
SPM Software & Resources
2nd Level Analysis Methods for Dummies 2010/11 - 2nd Feb 2011
Keith Worsley Keith Worsley
The statistical analysis of fMRI data using FMRISTAT and MINC
Bayesian Methods in Brain Imaging
Anatomical Measures John Ashburner
Mixture Models with Adaptive Spatial Priors
Will Penny Wellcome Trust Centre for Neuroimaging,
Presentation transcript:

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 2

DPARSF (Yan and Zang, 2010) 3

Data Processing Assistant for Resting-State fMRI (DPARSF) Yan and Zang, 2010. Front Syst Neurosci. http://rfmri.org/DPARSF 4

DPABI: a toolbox for Data Processing & Analysis of Brain Imaging License: GNU GPL Chao-Gan Yan Programmer Initiator Xin-Di Wang Programmer http://rfmri.org/dpabi http://dpabi.org 5

Outline Overview Data Preparation Preprocessing R-fMRI measures Calculation Quality Control Statistical Analysis Results Viewing 6

Data Organization ProcessingDemoData.zip FunRaw T1Raw Sub_001 Sub_002 Sub_003 T1Raw Functional DICOM data Structural DICOM data http://rfmri.org/DemoData 7

Data Organization ProcessingDemoData.zip FunImg T1Img Sub_001 Sub_002 Sub_003 T1Img Functional NIfTI data (.nii.gz., .nii or .img) Structural NIfTI data (.nii.gz., .nii or .img) 8

9

10

Outline Overview Data Preparation Preprocessing R-fMRI measures Calculation Quality Control Statistical Analysis Results Viewing 11

12

Preprocessing Working Dir where stored Starting Directory (e.g., FunRaw) Detected participants 13

Detected participants Preprocessing Detected participants 14

(if 0, detect automatically) (if 0, detect from NIfTI header) Preprocessing Number of time points (if 0, detect automatically) TR (if 0, detect from NIfTI header) Template Parameters 15

Resting State fMRI Data Processing Template Parameters 16

What’s new? 17

Reorient and Quality control 18

19

For checking EPI coverage and generating group mask Automask generation For checking EPI coverage and generating group mask FunImgAR/Sub_001 Masks/AutoMasks/ 20

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. 21

Bet & Coregistration Segment bet Apply Coregister T1ImgCoreg/Sub_001 T1Img/Sub_001 RealignParameter/Sub_001/mean*.nii Apply Coregister RealignParameter/Sub_001/Bet_mean*.nii T1ImgBet/Sub_001 22

Nuisance regression 23

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 24

Outline Overview Data Preparation Preprocessing R-fMRI measures Calculation Quality Control Statistical Analysis Results Viewing 25

R-fMRI measures Calculation 26

Outline Overview Data Preparation Preprocessing R-fMRI measures Calculation Quality Control Statistical Analysis Results Viewing 27

28

Quality Control 29

Quality Control 30

Quality Control 31

Quality Control 32

Quality Control 33

Quality Control 34

Quality Control 35

Quality Control 36

Quality Control 37

Quality Control 38

Quality Control 39

Quality Control 40

This mask is very important for group statistical analysis!!! Quality Control This mask is very important for group statistical analysis!!! 41

Quality Control 42

Quality Control 43

Quality Control 44

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 45

Outline Overview Data Preparation Preprocessing R-fMRI measures Calculation Quality Control Statistical Analysis Results Viewing 46

47

Statistical Analysis 48

{DPABI_Dir}/StatisticalAnalysis/y_GroupAnalysis_Image.m 49

Attention!!! 50

{DPABI_Dir}/StatisticalAnalysis/y_GroupAnalysis_Image.m Smoothness estimation based on the 4D residual is built in this function!!! 51

{Download}/ProcessingDemoData/StatisticalDemo/AD_MCI_NC/ 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: 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!!! 52

Outline Overview Data Preparation Preprocessing R-fMRI measures Calculation Quality Control Statistical Analysis Results Viewing 53

54

55

56 56

57

58 58

59

60 60

Voxel Z > 2.3, Cluster P < 0.05, Two One-Tailed Corrections: equivalent to Voxel P < 0.0214, Cluster P < 0.1, Two Tailed. 61

62

63

64 64

65 65

66 66

67 67

Further Help Further questions: http://rfmri.org/dpabi http://dpabi.org The R-fMRI Network 68 68

Further Help 69 69

70 70

71 71

Send emails only to rfmri. org@gmail 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 R-fMRI nodes will receive email updates of your posts. 72 72

Xin-Di Wang Programmer Yu-Feng Zang Consultant

Acknowledgments Nathan Kline Institute Charles Schroeder Stan Colcombe Gary Linn Mark Klinger Hangzhou Normal University Yu-Feng Zang Beijing Normal University Yong He NYU Child Study Center F. Xavier Castellanos Adriana Di Martino Clare Kelly Fudan University Tian-Ming Qiu Chinese Academy of Sciences Xi-Nian Zuo Different Axis!!! Child Mind Institute Michael P. Milham R. Cameron Craddock Zhen Yang Princeton University Han Liu

Advertisement Charles E. Schroeder F. Xavier Castellanos NKI/Columbia NKI/NYU Charles E. Schroeder NKI/Columbia David A. Leopold NIMH 75

Thanks for your attention! 76