fmri資料分析講習：...

fMRI資料分析講習：SPM實作

薛人瑞博士

心智影像研究中心

2017.4.15

Some parameters we should know TR = 2 s

TE = 35 ms

Number of Slice = 32

Slice order = sequential or interleave

Number of dummy scan = 5

Scan Time= 310 s

5 dummies * 2 s = 10 s

+

150 scans * 2 s = 300 s

Some parameters we should know Resolution = FOV/Matrix size

– in-plane resolution = 3.5 * 3.5 mm (224/64=3.5)

– voxel resolution = 3.5 * 3.5 * 3 mm (224/64=3.5)

FOV =

224 mm

64

64

224 mm

Slice thickness

3 mm

3.5 mm

3.5 mm

3 mm

DICOM?TR

out.nii or

out.img

Slice

DICOM

(Digital Imaging and

Communication in

Medicine)

為醫療數位影像傳輸協定與標準

DICOM format

+

http://people.cas.sc.edu/rorden/dicom/index.html

MicroDicom

Matlab Info = dicominfo(‘data.dcm’);

data = dicomread(info);

Matlab imshow(data,[]);

Basic concept

Functional image (EPI )

Structure image (T1-weighted image )

Quality

Assurance

fMRI Data

Preprocessing

fMRI Data

Analysis

Results

Visualization

Software

Preparation

Quality assurance

brightening of the center of the

image compared with the

periphery.

white pixels

visual inspection

abnormal

Quality assurance - visual inspection

Basic concept



Quality

Assurance

fMRI Data

Preprocessing

fMRI Data

Analysis

Results

Visualization

Software

Preparation

SPM setup SPM embedded in MatLab

http://www.fil.ion.ucl.ac.uk/spm/

SPM

Installation:

1. Unzip spm8.zip

2. Copy to any folder you want

3. Set path (important)

4. Unzip spm8_updates and overwrite to installed folder

or >> spm_update update

Basic concept



Quality

Assurance

fMRI Data

Preprocessing

fMRI Data

Analysis

Results

Visualization

Software

Preparation

Experimental Paradigm

Close Open Close Open Close Open

0 50 100 150 200 250 300 Second

0 25 50 75 100 125 150 TR (scan)

TR/TE = 2 s/ 35ms


Scan Time= 310 s (5 dummies + 150 scans)

FOV = 224 mm

Voxel size = 3.75 * 3.75 * 4 mm

Data Preprocessing for fMRI

DICOM Convert

Slice Timing

Realignment

Coregistration

Normalization

Smoothing

DICOM ConvertTR

out.img

Slice

Slice number = 32 per TR (2s)

Check data

- Display: 1 image

- Check Reg: >1 & <15 images


DICOM Convert

Slice Timing

Realignment

Coregistration

Normalization

Smoothing

Slice Timing

Sladky et al, NeuroImage 2011,58:588-594

Slice Timing

Reference

https://en.wikibooks.org/wiki/Neuroimaging_Data_Processing/Slice_Timing

TA: the time between acquisition of the first slice and

the last slice

Slice order: 1:2:32 2:2:32

The reference slice is usually acquired in the

middle of the sequence, but any slice can be

used


DICOM Convert

Slice Timing

Realignment

Coregistration

Normalization

Smoothing

Realignment

ZX

Y

Z axis: up & down

X axis: left & right

Y axis: forward & backward

Translation

Rotation

Reference

https://en.wikibooks.org/wiki/Neuroimaging_Data_Processing/Realignment

Output.img

• rp_asNCKU.txt

• ImCalc

asNCKU.img rasNCKU.img

-

=

Criteria for head motion

• Ideal

- < 0.5-0.75 mm

- < 0.5o

• Acceptable

< 1 voxel

< 1o

Slice timing or head motion

correction first?

With a sequential slice order

- motion correction first

With an interleaved slice order:

- slice timing correction first

Slice timing correction generally isn’t as important for block design

Reference

https://en.wikibooks.org/wiki/Neuroimaging_Data_Processing/Slice_Timing

Ready for functional analysis?

No!!!

Such corrections are sufficient for functional analysis to a single subject.

Two important questions:

1. how does activity map onto anatomy?

2. how consistent is that mapping across subjects?


DICOM Convert

Slice Timing

Realignment

Coregistration

Normalization

Smoothing

Coregistration Align fMRI (EPI) with structural images (T1).

Align T1 to EPI


DICOM Convert

Slice Timing

Realignment

Coregistration

Normalization

Smoothing

Normalization

Normalized EPI

Normalized T1Template (T1)

Template (EPI)

Transformation

parameter

Subject T1

Subject EPI

Normalization (T1 to template)

Coregistrated T1

T1.nii, 1

Normalization (EPI to template)

Transformation

parameter

Default

-78 -112 -50

78 76 85

-90 -126 -72

90 90 108


DICOM Convert

Slice Timing

Realignment

Coregistration

Normalization

Smoothing

Smoothing

Advantages:

- Increase the signal-to-noise ratio (SNR)

- Decrease the noise

- Improve the validity of statistical techniques

- Can help to reduce the mismatch across subjects

Disadvantages:

- Reduces the spatial resolution, more blurrier

- Peaks are squashed

Normalized Normalized + smoothed

Twice of voxel size

Check data

- Display: 1 image

- Check Reg: >1 & <15 images


DICOM Convert

Slice Timing

Realignment

Coregistration

Normalization

Smoothing

DICOM Convert

Slice Timing

Realignment

Coregistration

Normalization

Smoothing

Batch

Batch

dicom-to-img files

T1 images

Subject folder

Create your own batch

Batch for multi-subject

One subjectBatch file

Basic concept



Quality

Assurance

fMRI Data

Preprocessing

fMRI Data

Analysis

Results

Visualization

Software

Preparation

1st level analysis:

A within-subjects analysis where activation is averaged across scans

for an individual subject

2nd level analysis:

A between-subject analysis is referred to as a 2nd level analysis.

Key concept

Key concept General linear model (GLM)

𝑌

= 𝑌 × 𝑌 + 𝑌Dependent

Variable

(What you are

measuring)

Independent

Variable

(What you are

manipulating)

Relative Contribution

(These need to be estimated)Error

Matrix of BOLD signals

(preprocessed data)

Design matrix

(regressors,

This is what is

put into SPM)

Matrix parameters

(These need to be estimated)

Error matrixfMRI

analysis

Regressors:

- contributors in your experiment

Regressors of interest:

- experimental regressors which you manipulated

Regressors of non-interest:

- nuisance regressors which are not related to the experimental hypotheses

E.g. - The 6 movement regressors (rotations x3 & translations x3 ) or physiological factors

e.g. heart rate

Key concept

Design matrix Modeling Hemodynamics response

Hemodynamic response function (HRF)

Unit step function

convolve

Experimental Paradigm

Close Open Close Open Close Open

0 50 100 150 200 250 300 Second

0 25 50 75 100 125 150 TR (scan)

TR/TE = 2 s/ 35ms


Scan Time= 310 s (5 dummies + 150 scans)

FOV = 224 mm

Voxel size = 3.75 * 3.75 * 4 mm

1st level analysis

Scans/seconds

Preprocessed data

0

50

100

150

200

250

300

(s)

Check:

Onset

Duration

Realignment parameters

SPM.mat

Key concept General linear model (GLM)

𝑌

= 𝑌 × 𝑌 + 𝑌Dependent

Variable

(What you are

measuring)

Independent

Variable

(What you are

manipulating)

Relative Contribution

(These need to be estimated)Error

Matrix of BOLD signals

(preprocessed data)

Design matrix

(regressors,

This is what is

put into SPM)

Matrix parameters

(These need to be estimated)

Error matrixfMRI

analysis

SPM.mat

SPM.mat

One-side test Two-side test

Delete contrasts?

NO!!!

Redo estimate

Do all the contrasts!!

The map of T-values :

spmT_*.img

The contrast itself:

con_*.img

2nd level analysis

Contrasts =

combination of beta values

Basic concept



Quality

Assurance

fMRI Data

Preprocessing

fMRI Data

Analysis

Results

Visualization

Software

Preparation

Normalized or

Template images

Spm8\rend\

Spm8\canonical\

xjView

xjView

Reference

http://www.alivelearn.net/xjview/xjView 8 Manual.pdf

Marsbar

SPM.mat

Marsbar

Marsbar Reopen

Subj01

Subj02

Subj03

con

tras

t im

ages

Con_*.img

N=3

One-sample

t-test

p < 0.001 (uncorrected)

SPM{t}

1st-level (within-subject) 2nd-level (within-subject)

2nd level analysis

EO > EC

The question: is mean activation significantly greater

than zero?

Two-sample t-test

Paired t-test

One subject for one pair

Event-related analysis Preprocessing

TR/TE = 2 s/ 35ms


Scan Time= 600 s

(5 dummies + 295 scans)

FOV = 224 mm

Voxel size = 3.75 * 3.75 * 4 mm

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