Etudes de Connectivités fonctionnelles et effectives

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Etudes de Connectivités Etudes de Connectivités fonctionnelles et effectives fonctionnelles et effectives Oury Monchi, Ph.D. Oury Monchi, Ph.D. Unité de Neuroimagerie Unité de Neuroimagerie Fonctionnelle, Centre de Fonctionnelle, Centre de Recherche, Institut Universitaire Recherche, Institut Universitaire de Gériatrie de Montréal & de Gériatrie de Montréal & Université de Montréal Université de Montréal

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Etudes de Connectivités fonctionnelles et effectives. Oury Monchi, Ph.D. Unité de Neuroimagerie Fonctionnelle, Centre de Recherche, Institut Universitaire de Gériatrie de Montréal & Université de Montréal. Les analyses que nous avons étudiées jusqu’à mainteant - PowerPoint PPT Presentation

Transcript of Etudes de Connectivités fonctionnelles et effectives

Page 1: Etudes de Connectivités fonctionnelles et effectives

Etudes de Connectivités fonctionnelles et Etudes de Connectivités fonctionnelles et effectiveseffectives

Oury Monchi, Ph.D.Oury Monchi, Ph.D.

Unité de Neuroimagerie Fonctionnelle, Unité de Neuroimagerie Fonctionnelle, Centre de Recherche, Institut Universitaire Centre de Recherche, Institut Universitaire

de Gériatrie de Montréal & de Gériatrie de Montréal &

Université de MontréalUniversité de Montréal

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• Les analyses que nous avons étudiées jusqu’à mainteantnous permettent d’évaluer si l’activité d’une une ou

plusieurs régions du cerveau augmentent de matière significative dansune condition par rapport ou une autre

• Durant les travaux pratiques vous avez aussi vu commentreconstruire le signal BOLD pour une région et une

condition donnée

• Ceci dit ces anlyses ne nous permettent pas d’avoir d’information sur les intéractions entre différentes régions

du cerveau pendant que l’on performe une tâche

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Études de connectivitées

A. Analyses de données IRMf 1. Connectivitée fonctionnelle

2. Connectivitée effective

B. Fusions multimodales 1. TMS/PET 2. TMS/fMRI

C. Imagerie par Tenseur de Diffusion Dr. Thomas Jubault (12 Mars)

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Structure – Function Relationships Functional Segregation

Where are regional responses to experimental input?Univariate analyses of regionally specific effects

Functional integration

How does one region influence another (coupling b/w regions)?

How is coupling effected by experimental manipulation (e.g. attention)?

Multivariate analyses of regional interactions

Experimentally designed input

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System analyses in functional neuroimaging

System analyses in functional neuroimaging

Functional integrationAnalyses of Analyses of inter-regional effectsinter-regional effects: : what are the interactions what are the interactions between the elements of a given between the elements of a given neuronal system?neuronal system?

Functional integrationAnalyses of Analyses of inter-regional effectsinter-regional effects: : what are the interactions what are the interactions between the elements of a given between the elements of a given neuronal system?neuronal system?

Functional connectivity= the temporal correlation = the temporal correlation betweenbetween spatially remote spatially remote neurophysiologicalneurophysiological events events

Functional connectivity= the temporal correlation = the temporal correlation betweenbetween spatially remote spatially remote neurophysiologicalneurophysiological events events

Effective connectivity= the influence that the elements = the influence that the elements

of a neuronal system exert over of a neuronal system exert over anotheranother

Effective connectivity= the influence that the elements = the influence that the elements

of a neuronal system exert over of a neuronal system exert over anotheranother

Functional specialisationAnalyses of Analyses of regionally specific regionally specific effectseffects: which areas constitute a : which areas constitute a neuronal system?neuronal system?

Functional specialisationAnalyses of Analyses of regionally specific regionally specific effectseffects: which areas constitute a : which areas constitute a neuronal system?neuronal system?

MECHANISM-FREE MECHANISTIC MODEL

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• Aims – Summarise patterns of correlations among brain systems

– Find those spatio-temporal patterns of activity which explain most of the variance in a series of repeated measurements

(e.g. several scans in multiple voxels)

• Procedure– Select those voxels whose activation levels show a significant difference between the

conditions of interest

– Calculate the covariance matrix

– Principle Component Analysis (PCA) is a Singlular Value Decomposition (SVD) of the covariance matrix. This produces Eigenimages

Functional Connectivity: The Basics

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Functional connectivity: methods

• Seed-voxel correlation analyses

• Eigenimage analysis– Principal Components Analysis (PCA)– Singular Value Decomposition (SVD)– Partial Least Squares (PLS)

• Independent Component Analysis (ICA)

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Pros & Cons of functional connectivity

• Pros:– useful when we have no model of what

caused the data (e.g. sleep, hallucinatons, etc.)

• Cons:– no mechanistic insight into the neural

system of interest– inappropriate for situations where we have

a priori knowledge and experimental control about the system of interest

models of effective connectivity necessary

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PPI example: attentional modulation of V1→V5

V1V1

attention

no attention

V1 activity

V5 a

ctiv

ity

SPM{Z}

time

V5 a

ctiv

ity

Friston et al. 1997, NeuroImage 6:218-229Büchel & Friston 1997, Cereb. Cortex 7:768-778

V1 x Att.V1 x Att.

=

V5V5

V5V5

Attention

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PPI: interpretation

Two possible interpretations of the PPI

term:

V1

Modulation of V1V5 by attention

Modulation of the impact of attention on V5 by V1.

V1V1 V5V5 V1

V5V5

attentionattention

V1V1

attentionattention

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Pros & Cons of PPIs• Pros:

– given a single source region, we can test for its context-dependent connectivity across the entire brain

• Cons:– very simplistic model:

only allows to model contributions from a single area – ignores time-series properties of data– not easily used with event-related data– operates at the level of BOLD time series

limited causal interpretability in neural terms,more powerful models needed

DCM!

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Functional and Effective Connectivity

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Models of effective connectivity = system models.But what precisely is a system?

• System = set of elements which interact in a spatially and temporally specific fashion.

• System dynamics = change of state vector in time

• Causal effects in the system:– interactions between elements– external inputs u

• System parameters :specify the nature of the interactions

• general state equation for non-autonomous systems

)(

)(

)(1

tz

tz

tz

n

),,( uzFz

overall system staterepresented by state variables

« z 1« z n

f1(z1...zn,u,1)

fn (z1...zn,u,n )

change ofstate vector

in time

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Practical steps of a DCM study - I1. Conventional SPM analysis (subject-specific)

• DCMs are fitted separately for each session → consider concatenation of sessions or

adequate 2nd level analysis

2. Definition of the model (on paper!)• Structure: which areas, connections and inputs?• Which parameters represent my hypothesis?• How can I demonstrate the specificity of my results?• What are the alternative models to test?

3. Defining criteria for inference:• single-subject analysis: stat. threshold? contrast?• group analysis: which 2nd-level model?

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Stimuli 250 radially moving dots at 4.7 degrees/s

Pre-Scanning 5 x 30s trials with 5 speed changes (reducing to 1%)Task - detect change in radial velocity

Scanning (no speed changes)6 normal subjects, 4 x 100 scan sessions;each session comprising 10 scans of 4 different conditions

F A F N F A F N S .................

F - fixation point onlyA - motion stimuli with attention (detect changes)N - motion stimuli without attentionS - no motion

Büchel & Friston 1997, Cereb. CortexBüchel et al. 1998, Brain

V5+

PPCV3A

Attention – No attention

Attention to motion in the visual system

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V1

IFG

V5

SPC

Motion

Photic

Attention

0.88

0.48

0.37

0.72

0.42

0.66

0.56

0.55•Visual inputs drive V1, Visual inputs drive V1,

activity then spreads to activity then spreads to hierarchically arranged hierarchically arranged visual areas.visual areas.

•Motion modulates the Motion modulates the strength of the V1→V5 strength of the V1→V5 forward connection.forward connection.

•The intrinsic connection The intrinsic connection V1→V5 is insignificant in V1→V5 is insignificant in the absence of motion the absence of motion (a(a2121=-0.05).=-0.05).

•Attention increases the Attention increases the backward-connections backward-connections IFGIFG→SPC and SPC→V5. →SPC and SPC→V5.

A simple DCM of the visual system

0.26

-0.05

Re-analysis of data fromFriston et al., NeuroImage 2003

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V1

V5

SPC

Motion

Photic

Attention

0.85

0.57 -0.02

1.360.70

0.84

0.23

V1

V5

SPC

Motion

PhoticAttention

0.86

0.56 -0.02

1.42

0.550.75

0.89V1

V5

SPC

Motion

PhoticAttention

0.85

0.57 -0.02

1.36

0.030.70

0.85

Attention0.23

Model 1:attentional modulationof V1→V5

Model 2:attentional modulationof SPC→V5

Model 3:attentional modulationof V1→V5 and SPC→V5

Comparison of three simple models

Bayesian model selection: Model 1 better than model 2,

model 1 and model 3 equal

→ Decision for model 1: in this experiment, attention

primarily modulates V1→V5

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Transcranial Magnetic Stimulation

•TMS involves placing an electromagnetic coil on the subject scalp.• High-intensity current is rapidly turned on and off in the coil through the discharge of capacitors.

•The current flowing briefly in the coil generates a changing magnetic field that induces an electric current in the neural tissue, in the opposite direction.

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Stimulators and Coils

Single-pulse TMS

Paired-pulse TMS

Repetitive TMS

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TMS coil

TMS and Functional Imaging (PET) [15O] H2O and [11C]raclopride

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Frameless StereotaxyTMS and PET

• Precise localization of the TMS coil relative to the brain is critical for the interpretation of brain-mapping studies

• This is best achieved by acquiring a structural MR image of the subject’s brain and using the image to guide positioning of the coil in real time.

• The frameless stereotactic system allows to co-register the subject's MRI with the head's surface and in a second step with the location of the TMS coil on the scalp.

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3 ms ISI12 ms ISIt

3.0

3.8t

3.6

3.0

Z= 61Z= 48

Paired-pulse TMS/ [15O] H2O PET

Strafella and Paus, J. Neurophysiol. 2001

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-3

-6t

Reductions in [11C]raclopride BP

• 7.4% reduction in ipsilateral caudate

• No change in contralateral caudate

• No change in putamen, accumbens

ROI Analysis

Cortical Control of Dopamine release

Strafella et al., J. Neurosci. 2001

X

dorsolateral PFC

X

occipital

TMS and PET [11C] raclopride

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-3

-6t

• 9.4% reduction in ipsilateral putamen

• No change in contralateral putamen

• No change in caudate, accumbens

ROI Analysis

Strafella et al., Brain 2003

Reductions in [11C]raclopride BP

X

occipital

X

Primary motor cortex

TMS and PET [11C] racloprideCortical Control of Dopamine release

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IRMf et TMS ‘offline’

Continuous Theta Burst Stimulation (cTBS)

• 80% active motor threshold

• Similar to slow rTMS– Suppresses the

cortico-excitability

40 sec (600 pulses)

50Hz TBS

50Hz TBS

50Hz TBS

50Hz TBS

50Hz TBS

200ms 200ms 200ms 200ms

50Hz TBS

200ms

20 sec (300 pulses)

Huang et al. Neuron 2005

• Long lasting after-effect

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IRMf et TMS online

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Acknowledgements

SPM DCM courseDrs. Marcus Gray & Petra VetterDrs. Klaas Enno Stephan &Lee Harrison

Dr. Randy McInstosh, Dr. Barry Hortwitz

TMS/PETDr. Antonio P. Strafella, Ji-Hyun Ko