case study: single subject and group analysis
TRANSCRIPT
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7/28/2019 Case Study: Single Subject and Group Analysis
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Xin Di, PhD & Suril Gohel
New Jersey Institute of Technology
An example data - by Dr. Yu-Feng Zang
Resting-state: eye open vs. eye close
Preparing Data for analysis Preprocessing
Hypothesis
Data analysis
Local properties
Connectivity
Testing Hypotheses and Inferring Results Group inference
Eye open/Eye closed data from INDI(http://fcon_1000.projects.nitrc.org/indi/retro/BeijingEOEC.html)
24 Subjects
Eye open, 6min, 240 images
Eye close, 6min, 240 images
TR 2 second, Voxel size: 3.13.13.5 mm
Anatomical SPGR image (MPRAGE Image)Specific to resting-state fMRI
Similar to task fMRI
Generally is not needed Slice timing
Motion correction
Spatial processing Spatial normalization
Spatial smoothing
Temporal processing Noise removal
Filtering
Global Intensitynormalization/Global
Regression
Noise removal
Physiological noises
Head motion
Filtering
Usually 0.01 0.08 Hz
Global Intensitynormalization/Globalregression
Physiological noises
o Cardiac
o Respiratory
Two ways to model
o Recorded during scanning
o After Scanning from the
BOLD fMRI data
Noise removal
Physiological noises
Head motion
Filtering
Usually 0.01 0.08 Hz
Global Intensitynormalization/Globalregression
WM/CSF Signal
o High probability threshold
p > 0.99 not p > 0.5
o Use eroded WM/CSF masks
o Use unsmoothed fMRI data
o Mean time course or
principle components (Chai et
al., 2012)
http://fcon_1000.projects.nitrc.org/indi/retro/BeijingEOEC.htmlhttp://fcon_1000.projects.nitrc.org/indi/retro/BeijingEOEC.html -
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Noise removal
Physiological noises
Head motion Filtering
Usually 0.01 0.08 Hz
Global Intensitynormalization/Globalregression
Head motion
o Six rigid-body motion
parameters (translation androtation)
o First order derivatives
o Autoregressive model
(current and previous time
points) (Friston et al., 1995,
Yan et al., 2013)
Noise removal
Physiological noises
Head motion Filtering
Usually 0.01 0.08 Hz
Global Intensitynormalization/Globalregression
Global Intensity normalization
/Global Regression
o Will introduce negative correlations
(Murphy et al., 2009; Saad et al., 2012)
o Enhance neural (LFP)-hemodynamic
(BOLD) correlations of negative
connectivity (Keller et al., 2013)
o Reduce Inter-subject variance (Yan et
al. 2013)
o Use with precaution about what
negative correlation in the data
represents
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Raw time course Regress out covariates Band-pass filtering
First 5 PCs of WM First 5 PCs of CSF Motion parameters
Experiment
Eye open vs. Eye close
Goal
Functional specification
Local properties
Functional integration
Connectivity
Time-domain Properties
Standard deviation (Biswal et al., 2007)
Frequency-domain Properties
Amplitude of Low-Frequency Fluctuation (ALFF, Zang etal., 2007)
Fractional Amplitude of Low-Frequency Fluctuation (fALFF,Zou et al., 2008)
Homogenous Properties
Regional Homogeneity (ReHo, Zang et al., 2004)
Network centralities
Eigen vector centrality (ECM, Lohmann et al., 2010; Wink etal., 2012)
ALFF (Zang et al., 2007)
Band-Pass Filtering
Fourier transform
Square root
Average across 0.01 -0.08 Hz - ALFF
Divided by global mean mALFF Divided by whole
spectrum - fALFF (Zou etal., 2008) Zang et al., 2007
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ReHo (Zang et al.,2004)
Local similarity
Kendalls coefficient ofconcordance (KCC)
W=(R
i)2 n(R )
2
1
12K
2( n3 n)7 19 27
ECM (Lohmann et al.,2010)
The importance of agiven voxel in the WHOLEBRAIN network. Number of nodes connected
How important theconnected nodes
Fast ECM (Wink et al.,2012)
Wink et al., 2012
mfALFF
mReHomALFF
Eye open Eye close
ECM
Eye open Eye close
eye close> eye open
eye open> eye close
mALFF mfALFF mReHo ECM
p < 0.001, cluster level FDR p < 0.05
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