analysis and characterization of the physiological noise in ......i. introduction • fmri signals,...

University of Rome, La Sapienza Department of Physics

G1 Group

Analysis and characterization of the physiological noise in Resting state fMRI measurements

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I. INTRODUCTION• fMRI signals,• fMRI Noise,• Physiological Noise,• Techniques of Reduction Physiological Noise,• Resting State.

II. Physiological Noise Characterization

III. Graphic Theory Analysis

Outline

IMAGE ACQUISTIONIMAGE PREPROCESSING RESULTS

GRAPH THEORYMETHODRESULTS

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IINTRODUCTION

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Magnetic susceptibility difference across the blood vessels

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5

Change in CBF appear to more than is necessary to support the small increase in oxygen metabolism (∆CBF>>∆CMRO2)

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CBF

CMRO2

Task Task OnOn

Time

BOLD Signal

Deoxyhaemoglobin change

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Time courses of CBF and CMRO2 that would predict an early neagative BOLD response 6

I. INTRODUCTION

The signals of interest include:

task-related signal, function-related signal, and transiently task-related signal.

The signals not of interest include:

physiology-related signal, motion related signal, and scanner-related signal.

Functional magnetic resonance imaging (fMRI) is a noninvasive technique that uses Blood oxygen level dependent (BOLD) effect to explore neural activity.

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I. INTRODUCTION

Thermal Noise

System Noise

Motion Noise

External Noise

Physiological Noise

................. etc1 July 2010

Changes in signal intensity over time due to thermal motion of electrons within the subject and within the scanner electronics.

Arises as a result to static field inhomogeneities, nonlinearities and instabilities in the gradient field and variations in imaging hardware.

Severe head motion during an fMRI scan can severely corrupt the data e.g. eyes, head….etc

Interference from outside sources can also lead to distortions and artefacts in the data.

Cardiac and respiration noise.

Acoustic, Non- task related neural variability, .......

8

Cardiac

Resp.

Frequency (Hz )

cardiac here (~0.8Hz)

Resp. here (~0.15Hz)

Glover et al, 2000

fMRI Techniques and Protocols,2009

I. INTRODUCTION

TR= 250 msec.

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There are several methods have been developed for reducing such physiological noise in fMRI time series, including:

navigator echo (Hu X, Kim SG, 1994),

retrospective gating (Hu X, et al, 1995),

digital filtering (Biswal B, et al, 1996),

k-space based estimation and correction (Wowk B, et al 1997),

pulse sequence gating (Guimaraes AR, et al, 1998),

motion-ordered data acquisition (Stenger VA, et al, 1999),

RETROICOR (Glover GH, et al, 2000),

IMPACT (Chuang KH, et al, 2001),

CORSICA (perlbarg V, et al, 2007),

RVHRCOR (Chang G,et al, 2009)

I. INTRODUCTION

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Fox MD et al, PNAS 2005; 102, 9673-9678Greicius M et al, PNAS 2003; 100, 253-258Raichle M et al, PNAS 2001; 98, 676-682

I. INTRODUCTION

0.01 Hz < f < 0.1 Hz

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Intrinsic Correlations between PCC and all other Voxels in the Brain during Resting State

Resting state networksM. De Luca,2006;29, 1359-1367

IIPhysiological Noise Characterization

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ICA-based artifact removal in functional connectivity analysis

• Subjects: 13 healthy right-handed subjects (27±7 years), healthy and right handed

• MRI Scanner: 3T Allegra (Siemens, Erlangen, D)

EPI-SIEMENS:

• TR/TE=2100/30 ms

• 3x3x2.5 mm3

• 32 slices

• 240 vol

20 40 60 80 100 120 140 160 180 200 220 240

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Voxel’s time series



Movement Regression, Temporally band-pass filtered

(0.009 < f < 0.15), Detrend,Centering,Whiting

Statistical Parametric Mapping (SPM8)http://www.fil.ion.ucl.ac.uk/spm/

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Create CSF & WM Images (New approach)

CSF common mask WM common mask

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Extract CSF & WM Time Series (using ICA)

• 20 CSF time series

• 20 WM time series

fMRI Images CSF ImagesWM Images

CSF Time seriesWM Time series

CSF mask

WM mask

ICA

ICA

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“Independent component analysis (ICA) is a method for finding underlying factors or components from multivariate (multidimensional) statistical data. What distinguishes ICA from other methods is that it looks for components that are both statistically independent, and nonGaussian.”

A.Hyvarinen, A.Karhunen, E.Oja‘Independent Component Analysis’1 July 2010 17



=

fMRI Data

Tim

e

Space # IC

Tim

e

x = As

# IC

Space

Mixing Matrix Spatial MapsN x K

N= Num. of Scans

K= Num. of IC

N x M

N= Num. of Scans

M= Num. of Voxels

K x M

K= Num. of IC

M=Num. of Voxels1 July 2010 18



Avoid the Global Signal Effect.

Regress out CSF and WM from fMRI data .

Create Region of Interest (ROI)

PCC (0, -40, 30)

0 50 100 150 200-0.5

0

0.5

Images Number

PCC ROI1 July 2010

Average time series of ROI19



Create Z- maps.⎟⎟⎠

⎞⎜⎜⎝

⎛−+

∗=),,(1),,(1log5.0),,(

zyxrzyxrzyxZ

0.3

0.09

Z-map for first subject

Group analysis (for 13 Subjects)

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Group analysis (N=13) of regions having significant positive correlations (p<0.05 FWE) with the PCC.

none

Global

WM/CSF

CSF

WM

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none

Global

WM/CSF

CSF

WM

Regions having negative correlation (p<0.05 uncorrected) with the PCC in a group-level (N=13).

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WM CSF WM/CSF Global-60

-50

-40

-30

-20

-10

0

perc

ent o

f pos

itive

ly c

orre

late

d vo

xels

%

WM CSF WM/CSF Global100

101

102

103

104

105

perc

ent o

f neg

ativ

ely

cor

rela

ted

voxe

ls %

Spatial extent of (A) positive (r>0.15) and (B) negative (r<−0.15) correlations.

A B

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CSF

CSF/WM

2

0

2

0

2

0WM

F -maps for single Subject

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•Regions having negative correlation after WM/CSF correction overlapped substantially

with those having the greatest negative correlation magnitudes after global signal removal.

• The spatial extent of significant positive correlations is diminished after WM/CSF

correction is applied, and even further diminished after global signal removal. CSF signal

removal and WM signal correction also introduced slight decreases in the extent of

positive correlations.

• Global signal removal greatly increased the negative correlations over more widespread

regions of the brain, as well as inter-subject variability.

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As a further step we would like to compare this approach with other approaches:

RETROICOR

CORSICA

RVHRCOR

Discussion the difference between physiological signal regression results and our results.

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IIIGraphic Theory Analysis

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node

edge

Van den Heuvel et al., 2010

• Using graph theory, functional brain networks can be defined as a graph G=(V,E).

Functional brain network

• To characterize the topological properties of a network, a number of parameters have

been described:

• Node Degree (K),

• Degree Density,

• Degree Distribution,

• Clustering coefficient (Ci)

Ci= 2Ei / Ki(Ki-1)

C=1/N . ƩCi

• Path Length

• Other topological properties.

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Fox, et al. 2005


Intrinsically defined anticorrelated processing networks in the brain

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- 9 subjects.-13 ROI. “Fair, et al 2008”- Pajek software. “http://pajek.imfm.si/doku.php”

DMNCorrelation Matrix Topological propertiesGraph

Schematic illustration of the graph analysis.

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DMN_Rest graph DMN_Task graphNodes

Edges

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aMPFC L.Sup.F R.Sup.F vMPFC L.IT R.IT L.PHC R.PHC PCC Rsp L.LatP L.LatP R.LatP0

0.05

0.1

0.15

0.2

0.25

0.3

0.35

0.4

0.45

0.5

ROI

Clu

ster

ing

Coe

ff.

DMN-TaskDMN-Rest

Clustering coefficient for first subject (N=1)

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1 2 3 4 5 6 7 8 90

0.05

0.1

0.15

0.2

0.25

0.3

0.35

0.4

Subjects

Average clustering coefficient

R-DMNT-DMN

mean±SDR-DMN= 0.2526 ± 0.0533T-DMN= 0.2221± 0.0384

Average clustering coefficient for all subject (N=9)

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aMPFC L.Sup.F R.Sup.F vMPFC L.IT R.IT L.PHC R.PHC PCC Rsp L.LatP R.LatP Cereb0

0.05

0.1

0.15

0.2

0.25

0.3

0.35

0.4

DMN nodes

DMN-Task DMN-Rest

Average clustering coefficient for each ROI (N=9)

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aMPFC L.Sup.F R.Sup.F vMPFC L.IT R.IT L.PHC R.PHC PCC Rsp L.LatP R.LatP Cereb0

0.5

1

1.5

2

2.5

3

3.5

4

4.5

DMN nodes

DMN-Task DMN-Rest

Degree value for each ROI (N=9)

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The analysis of DMN topological properties demonstrates that the overall effect of the cognitive load is to weaken the network connectivity, without inducing a reduction of the cortical hubs participating to the network itself. Because of a decrease of the node degree for almost all the nodes, during working memory with respect to the resting condition, it can be speculated that the communication within the network is reduced in behalf of the best performance during the task. As already reported, among all the nodes, the PCC seems to be the center of the information processing within the DMN.


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We are currently working on the description of the scale-free distribution of functional connections,

Study Community structure in networks of functional connectivity,

Extract the restating state networks directly from fMRI signals at voxel resolution.

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Thanks for your attention

[email protected] [email protected]

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Matrix Formulation

Equation for scan j

Simultaneous equations forscans 1..N(J)

…that can be solvedfor parameters β1..P(L)

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Regressors

Scan

s

+

error

vecto

r

ε+

data ve

ctor

y

=

design

matr

ix

= X

β1

β2

β3

β4

β5

β6

β7

β8

β9

param

eters

β×

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this line is a 'model' of the data

µ

slope β

ε

Y = βx + µ + ε

Y

X

Brain region Abbreviations MNI Coordinates1. Medial prefrontal cortex (anterior) aMPFC -3,54,18 2. Left superior frontal cortex L.Sup.F -15,54,423. Right superior frontal cortex R.Sup.F 18,42,484. Medial prefrontal cortex (ventral) vMPFC -6,36,-95. Left inferior temporal cortex L.IT -60,-9,-246. Right inferior temporal cortex R.IT 57,0,-277. Left parahippocampal gyrus L.PHC -24,-18,-278. Right parahippocampal gyrus R.PHC 27,-18,-249. Posterior cingulate cortex PCC -3,-48,3010. Retrosplenial Rsp 9,-54,1211. Left lateral parietal cortex L.LatP -48,-69,3912. Right lateral parietal cortex R.LatP 48,-66,3613. Cerebellar tonsils Cereb -6,-54,-48

SEED REGIONS FOR DEFAULT NETWORK

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analysis and characterization of the physiological noise in ......i. introduction • fmri signals,...

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