functional connectivity: a practical introduction · resting state fmri is a useful technique for...

Functional connectivity:

a practical introduction

DANTE MANTINI

Department of Health Sciences and Technology – ETH Zurich

Department of Experimental Psychology – University of Oxford

Overview

• What is functional connectivity (FC)?

• Why do we study FC in subjects at rest?

• Which methods can we use to study FC?

• What are the neuronal underpinnings of FC?

• What can FC tell us about brain functioning?

• Is information on FC clinically relevant?

• How can we use FC to study brain diseases?

What is functional

connectivity (FC)?

Long-range interactions in the brain: fMRI connectivity

Gillebert and Mantini, The Neuroscientist 2013

Time (min)

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Brain network

Long-range interactions in the brain: fMRI connectivity


Complex dynamic system

Functional and structural connectivity

Temporal correlations in fMRI data (functional connectivity) are related

to anatomical connectivity patterns (Rubinov & Sporns 2009).

Rubinov and Sporns, 2009

anatomical connections temporal correlations in fMRI data

Some regions can show strongly correlated fMRI timecourses mediated

by indirect structural links (Greicius et al., 2009).

Why do we study FC

in subjects at rest?

Spontaneous and task-related activity

Most of the brain energy

consumption is related to intrinsic

activity that is not driven by

responses to external stimuli

(Raichle 2001).

Energy for spontaneous brain activity

Energy for neuronal responses

Energy associated with evoked

brain activity accounts for less

than 5% of the total brain energy

budget.

Resting state functional magnetic resonance imaging

A participant in a resting

state experiment is asked:

not to move (eyes, head,

arms, etc…)

to be as much relaxed as

possible

not to think about anything

in particular

Resting state fMRI is a useful technique for the study of brain

functioning both in healthy subjects and in patients, because these

are not required to perform any goal-directed task.

resting state fMRI

Which methods can we

use to study FC?

Functional connectivity methods

Two functional connectivity methods are commonly used:

• Seed-based connectivity analysis

• Independent component analysis (ICA)

Functional connectivity methods detect networks

of brain areas showing coherent increases and

decreases of activity.

Seed-based functional connectivity analysis

Definition of the regions of

interest (ROIs) -> seeds

He et al., 2007

seed-to-seed

connectivity

matrix

He et al. 2007

seed-to-brain connectivity maps

Fox et al., 2006

TPJ

pIPS

Seed-based functional connectivity analysis

Effects of different

preprocessing steps

Vincent et al., 2006

To improve the detection of functionally connected areas, the contribution of

artifacts in the fMRI data needs to be minimized:

1) removal of images with large degree of head motion (scrubbing)

2) high-pass filtering (e.g. f > 0.009 Hz)

3) subtraction of global brain signal (this is an optional step)

4) subtraction of average signal in white matter and cerebrospinal fluid

5) attenuation of head motion effects

6) low-pass filtering (e.g. f < 0.08 Hz)

7) spatial smoothing

Seed-based connectivity analysis

CSF correlation

(blue)

WM correlation

(orange)

Giove et al., Magnetic Resonance Imaging 2009

Effect of global signal

regression

Murphy et al., Neuroimage 2009

Contribution of WM and CSF

artifacts to fMRI signals

Resting state networks (RSNs)


Measured signal

Task Non task-related

activations

(e.g. arousal)

Pulsations Machine Noise

Independent Component Analysis (ICA)

Assumption: spatial pattern from sources of variability unrelated (independent)

Independent component analysis (ICA)

The fMRI data at each time point is considered a

mixture of activity from each component map

n

COMPONENT

MAPS

MEASURED

fMRI SIGNAL

‘mixing matrix’,

M

#1

#2

t = 1

t = 2

t = n

S

S

S

S

Mixing

time

McKeown et al., 1998

Independent component analysis (ICA)

ICA is a statistical

method that extracts

maximally independent

patterns of coherent

fMRI activity, which are

linearly mixed in the

data.

Each pattern,

composed of a time-

course and an

associated spatial map,

is named independent

component (IC).

time-course

spatial

map

time-course

spatial

map

Mantini et al., 2007

Example of independent components

Consistently

task-related

Transiently

task-related

Quasi-periodic Slowly-varying Slow head

movement

Abrupt head

movement

Activated Suppressed

Group-analysis based on ICA

IC maps from different subjects

The self-organizing group ICA (sogICA) method can be

used to match spatially corresponding ICs in different

subjects.

The similarity between two ICs is

measured by means of the spatial

correlation coefficient between the

IC maps.

A matrix of cross-correlations

across ICs is created, and used as

input for cluster analysis.

Esposito et al., 2005

Group-analysis based on ICA

Damoiseaux et al., 2006

What are the neuronal

underpinnings of FC?

Electrophysiological signatures of resting state networks

Mantini et al., PNAS 2007

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Visual network Self-referential network

Attention network Sensorimotor network

Default network Auditory network correlation correlation

correlation correlation

correlation correlation

Correlation between RSN timecourses and EEG band-limited power

Electrophysiological signatures of RSNs

Mantini et al., 2007

Long-range functional interactions have neurophsyiological underpinnings

Detecting brain networks using EEG/MEG

Mantini et al., Brain Connectivity 2011 Mantini et al., Brain Connectivity 2011

EEG/MEG analysis pipeline


Mantini et al., Brain Connectivity 2011 Brookes et al., PNAS 2011


Hipp et al., Nature Neuroscience 2012

De Pasquale et al., Neuron 2012


de Pasquale et al., Neuron 2012

What can FC tell us

about brain functioning?

Relationship between connectivity and behavior

Several studies modulated resting state

functional connectivity by manipulating task

conditions prior to rest.

Resting state functional connectivity may keep

functional systems in an active and efficient

state to aid future behavior (Varela et al., 2001;

Engel et al., 2001).

Lewis, Baldassarre et al., 2009 & Baldassarre, Lewis et al., 2012

Effects of ‘virtual’ lesions on functional connectivity

Transcranial magnetic stimulation (TMS) modulates neuronal activity beyond the

site of stimulation, impacting a distributed network of brain regions (Eldaief et al.,

2011; van der Werf et al., 2010).

The effects of TMS on connectivity depend on the type (e.g. frequency, intensity)

of stimulation (Eldaief et al., 2011).

Eldaief et al., 2011

Functional connectivity and behaviour after TMS

Watanabe et al., Human Brain Mapping 2013

Effect of magnetic stimulation on connectivity

Watanabe et al., Human Brain Mapping 2013

Excitatory rTMS at 200 Hz

Inhibitory rTMS at 20 Hz

Is information on FC

clinically relevant?

Motor network connectivity following brain lesions

Carter et al., Annals of Neurology 2010

Somatomotor network


Carter et al., Annals of Neurology 2010


Carter et al., Neuroimage 2012

Assessing the integrity of brain networks in stroke

Gillebert et al. Brain 2011

lesion

Stroke

patient

Healthy

controls

(N=62)

Dorsal attention

network

Reorganization of RSNs after brain lesion


Do the observed changes in brain networks depend on

the lesion location?

BRAIN MAP 1 (Control)

INTENSITY VECTOR 2 INTENSITY VECTOR 3

0.84 0.11

max

min

0

zscore

INTENSITY VECTOR 1

Assessing the integrity of resting state networks

BRAIN MAP 2 (Control)

BRAIN MAP 3 (Patient)

Spatial correlation Spatial correlation

Reorganization of RSNs after brain lesion


How can we use FC to

study brain diseases?

Autism spectrum disorders (ASD)

Language impairments in ASD

Language activations

23 controls

(14.3±1.3 yrs)

19 patients

(14±1.5 yrs)

conjunction

(patients+controls)

Verhoeven et al., in press


Resting state fMRI data – seed-based connectivity


Connectivity impairments in dyslexia

Boets et al., Science 2013



V1

AGR IFGR

IFGL

STGL MTGL

PACL

SMGL AGL

SMGR

MTGL STGR

PACR

SUBJECTS:

23 adults with diagnosis of dyslexia

22 matched normal readers

Summary

• The analysis of FC in the brain is central for the

understanding of the organized behavior of cortical

regions.

• Resting state FC is a useful technique for the study

of neurological and psychiatric diseases, as the

patients are not required to perform any task.

• Changes in resting state FC partly explain behavioral

impairments in neurological and psychiatric patients.

• Analyses of FC alterations may offer new insights

into the pathophysiology of brain diseases.

Avenues for future research

• Analysis of resting state FC may help develop novel

therapeutic interventions, for instance via

pharmacological treatments.

• Longitudinal studies are needed to enhance our

understanding of how pathological interactions among

brain areas relate to neurological and psychiatric

deficits.

• Computational models based on functional and

structural imaging data can support the design of

individualized treatment and recovery protocols.

Acknowledgements

Rik Vandenberghe

Gian Luca Romani Maurizio Corbetta Nicole Wenderoth

ETH Zurich (Switzerland)

Joshua Balsters

University Oxford (U.K)

Chieti University (Italy)

Marco Ganzetti

Glyn Humphreys Celine Gillebert

Sjoerd Ebisch

Stefan Sunaert

KU Leuven (Belgium)

Hans Op De Beeck Bart Boets

Resting State Functional Connectivity

in the Normal and Diseased Brain

THANK YOU

FOR YOUR ATTENTION

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