Download - Dynamic Causal Modelling
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Dynamic Causal ModellingDynamic Causal Modelling
Will Penny
Wellcome Department of Imaging Neuroscience, University College London, UK
Cyclotron Research Centre, University of Liege, April 2003
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Outline
Functional specialisation and integration
DCM theory
DCM for auditory word processing
DCM for category effects
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Outline
Functional specialisation and integration
DCM theory
DCM for auditory word processing
DCM for category effects
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Attention to Visual MotionAttention to Visual Motion
StimuliStimuli
250 radially moving dots at 4.7 degrees/s250 radially moving dots at 4.7 degrees/s
Pre-ScanningPre-Scanning
5 x 30s trials with 5 speed changes (reducing to 1%)5 x 30s trials with 5 speed changes (reducing to 1%)
Task - detect change in radial velocityTask - detect change in radial velocity
ScanningScanning (no speed changes) (no speed changes)
6 normal subjects, 4 100 scan sessions;6 normal subjects, 4 100 scan sessions;
each session comprising 10 scans of 4 different conditioneach session comprising 10 scans of 4 different condition
e.g. F A F N F A F N S .................e.g. F A F N F A F N S .................
F – fixationF – fixation
S – stationary dots S – stationary dots
N – moving dotsN – moving dots
A – attended moving dotsA – attended moving dots
1. Photic Stimulation, S-F2. Motion, N-S3. Attention, A-N
Experimental Factors
Buchel et al. 1997
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Functional Specialisation
Q. In what areas does the ‘motion’ factor change activity ?
Univariate Analysis
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AttentionAttention
V2V2
attention
no attention
V2 activity
V5
acti
vity
SPM{Z}
time
V5
acti
vity
Functional Integration
Q. In what areas is activity correlated with activity in V2 ?
Q. In what areas does the ‘attention’ factor change this correlation ?
Multivariate Analysis
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AttentionAttention
V2V2
Functional Integration
Q. In what areas is activity correlated with activity in V2 ?
Q. In what areas does the ‘attention’ factor change this correlation ?
PPI Question:
Psycho-PhysiologicalInteraction
Larger Networks:
1. Structural Equation Modelling (SEM)2. Dynamic Causal Modelling (DCM)
Activity = ‘Hemodynamic’ (SEM) = ‘Neuronal’ (PPI/DCM)
Gitelman et al. 2003
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Outline
Functional specialisation and integration
DCM theory
DCM for auditory word processing
DCM for category effects
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To estimate and make inferences about
(1) the influence that one neural system exerts over another (i.e. effective connectivity)
(2) how this is affected by the experimental context
Aim of DCM
Z2
Z4
Z3
Z5
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DCM Theory
A Model of Neuronal ActivityA Model of Hemodynamic ActivityFitting the ModelMaking inferences
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Model of Neuronal Activity
),( uzfz
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Nonlinear,systems-levelmodel
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Bilinear Dynamics
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Bilinear Dynamics: Positive transients
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u2
Z2
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DCM: A model for fMRI
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Causality: set of differential equations relatingchange in one areato change inanother
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ssignal
infflow
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signal BOLD
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activity u s
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The hemodynamic model
O u t p u t f u n c t i o n : a m i x t u r e o f i n t r a - a n d e x t r a - v a s c u l a r s i g n a l
)1()/1()1(),,()( 32100 vkvqkqkVEqvty
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F lo w in d u c in g s ig n a l
sf in
State Equations
Buxton,Mandeville,Hoge,Mayhew.
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Hemodynamics
Impulseresponse
BOLD is sluggish
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Model estimation and inference
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Unknown neural parameters, N={A,B,C}Unknown hemodynamic parameters, HVague priors and stability priors, p(N) Informative priors, p(H)Observed BOLD time series, B.Data likelihood, p(B|H,N) = Gauss (B-Y)
Bayesian inference p(N|B) p(B|N) p(N)
LaplaceApproximation
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Posterior Distributions
CuuBzAzz
A1 A2 WA
C
P(A(ij)) = N (A(i,j),ij))
P(B(ij)) = N (B(i,j),ij))
P(C(ij)) = N (C(i,j),Cij))
Show connections for which A(i,j) > Threshwith probability > 90%
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1) Standard Analysis of fMRI Data
2) Statistical Parametric Maps
3) Construction of a Connectivity Model
4) Evaluation of the Connectivity Model
Design matrix
SPMs
Practical Steps of DCM
Z2
Z4
Z3
Z5
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Outline
Functional specialisation and integration
DCM theory
DCM for auditory word processing
DCM for category effects
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Single word processing at different rates
SPM{F}
“Dog”
“Mountain”
“Gate”
Functional localisation of primary and secondary auditory cortex and Wernicke’s area
Friston et al.2003
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Time Series
A1
WA
A2Auditory stimulus, u1
Adaptation variable, u2
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Dynamic Causal Model
A2
WA
A1
.
.
Auditory stimulus, u1
Model allows forfull intrinsicconnectivity
u1 Adaptation variable, u2
u1 enters A1 and is also allowed to affect all intrinsic self-connections
CuuBzAzz
u2 is allowed to affect all intrinsic connections betweenregions
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Inferred Neural Network
A2
WA
A1
.92(100%)
.38(94%)
.47(98%)
.37 (91%)
-.62 (99%)
-.51 (99%)
.37 (100%)
Intrinsic connectionsare feed-forward
Neuronal saturationwith increasing stimulus frequencyin A1 & WA
Time-dependentchange in A1-WAconnectivity
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Outline
Functional specialisation and integration
DCM theory
DCM for auditory word processing
DCM for category effects
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The fMRI data were originally acquired by Ishai et al. (1999; 2000) and provided by the National fMRI Data Center (www.fmridc.org)
2x3 Factorial Design:
Tasks were (1) passive viewing (2) delayed matching Stimuli were pictures of(1) Houses(2) Faces (3) Chairs
Baselines involved scrambled pictures of Houses, Faces and Chairs
DCM: Category Effects Mechelli et al. 2003
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ResultsIshai et al. found that...
(1) all categories activated a distributed system including bilateral fusiform, inferior occipital, mid-occipital and inferior temporal regions
(2) within this network, distinct regions in the occipital and temporal cortex responded preferentially to Faces, House and Chairs
L R
Medial Fusiform
Lateral Fusiform
Inferor Temporal
p<0.05 (corrected)
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Are the category effects reported by Ishai et al. (1999; 2000) in the occipital and temporal cortex
mediated by Bottom-up or Top-down mechanisms?
QUESTION:
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(1) V3 and the Superior Parietal area (that did not show category effects) (2) Temporal and Occipital areas (that did show category effects)
(3) Extrinsic connections
(4) Intrinsic Connections
(5) Modulatory Connections
Chair responsive
area
V3
SuperiorParietal
Houseresponsive
area
Visual Objects
Category Effects
DCM was used to estimate Extrinsic, Intrinsic and Modulatory connectionsat the neuronal level using Bayesian framework. Inferences were made at 95%
DCM Model
Faceresponsivearea
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We hypothesised a significant influence of category on the intrinsic connections which would account for the category effects observed in the occipital and temporal cortex.
(i) One possibility was that this influence would be expressed through the connections from V3 to the category-responsive areas – which would suggest bottom-up modulation.
(ii) Another possibility was that the influence of object category on the connectivity parameters was expressed in the connections from parietal cortex to the category-responsive areas – thereby indicating top-down modulation.
(iii) Finally, it was possible that object-specificity was conferred by connections from both V3 and parietal cortex.
Hypothesis
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DCM ResultsThe extrinsic connection from the experimental input to V3 was significant in all subjects
V3
Sup Par
Visual Objects
LateralFusiform
InferiorTemporal
Medial
Fusiform
Face responsive
area
Chair responsive
area
House responsive
area
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DCM ResultsThe intrinsic connections between V3, superior parietal and the category-responsive regions were significant
V3
Sup Par
Visual Objects
LateralFusiform
InferiorTemporal
Medial
Fusiform
Face responsive
area
Chair responsive
area
House responsive
area
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DCM ResultsThe modulatory connections showed that category effects in the occipital and temporal cortex were mediated by inputs from V3.
V3
Sup Par
Visual Objects
LateralFusiform
Face responsive
area
InferiorTemporal
Chair responsive
area
MedialFusiform
House responsive
area
Equivalent top-down effectwas not significant
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DCM ResultsThe modulatory connections showed that category effects in the occipital and temporal cortex were mediated by inputs from V3.
V3
Sup Par
Visual Objects
LateralFusiform
Face responsive
area
InferiorTemporal
Chair responsive
area
MedialFusiform
House responsive
area
Equivalent top-down effectwas not significant
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DCM ResultsThe modulatory connections showed that category effects in the occipital and temporal cortex were mediated by inputs from V3.
V3
Sup Par
Visual Objects
LateralFusiform
Face responsive
area
InferiorTemporal
Chair responsive
area
MedialFusiform
House responsive
area
Equivalent top-down effectwas not significant
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Summary
Studies of functional integration look at
experimentally induced changes in connectivityIn PPI’s and DCM this connectivity is at the
neuronal levelDCM: Neurodynamics and hemodynamicsInferences about large-scale neuronal networks