case study: single subject and group analysis

7/28/2019 Case Study: Single Subject and Group Analysis

1/7

5/31/20

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


o Cardiac

o Respiratory

Two ways to model

o Recorded during scanning

o After Scanning from the

BOLD fMRI data

Noise removal


Head motion

Filtering



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


2/7

5/31/20

Noise removal


Head motion Filtering



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


Head motion Filtering



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

0 50 100 150 200 2501030

1035

1040

1045

1050

1055

0 50 100 150 200 250-8

-6

-4

-2

0

2

4

6

0 50 100 150 200 250-6

-4

-2

0

2

4

6

0 50 100 150 200 250

-800

-600

-400

-200

0

200

400

600

800

0 50 100 150 200 250

-300

-200

-100

0

100

200

300

0 50 100 150 200 250

-0.5

-0.4

-0.3

-0.2

-0.1

0

0.1

0.2

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


3/7

5/31/20

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

i

-

,-

,-

case study: single subject and group analysis

Documents