Etudes de Connectivités fonctionnelles et effectives

1. 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

2. Les analyses que nous avons étudiées jusqu’à mainteant • nous 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 comment • reconstruire 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

3. Études de connectivitées 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)

4. Experimentally designed input 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 Functional Segregation Where are regional responses to experimental input? Univariate analyses of regionally specific effects Structure – Function Relationships

5. System analyses in functional neuroimaging Functional specialisation Analyses of regionally specific effects: which areas constitute a neuronal system? Functional integration Analyses of inter-regional effects: what are the interactions between the elements of a given neuronal system? Functional connectivity = the temporal correlation between spatially remote neurophysiological events Effective connectivity = the influence that the elements of a neuronal system exert over another MECHANISM-FREE MECHANISTIC MODEL

6. Functional Connectivity: The Basics • 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

7. 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)

8. 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

9. SPM{Z} V5 activity time V1 V5 V5 attention V5 activity no attention V1 activity PPI example: attentional modulation of V1→V5 Attention = V1 x Att. Friston et al. 1997, NeuroImage 6:218-229 Büchel & Friston 1997, Cereb. Cortex 7:768-778

10. V1 V5 V5 V1 attention attention PPI: interpretation Two possible interpretations of the PPI term: V1 V1 Modulation of V1V5 by attention Modulation of the impact of attention on V5 by V1.

11. 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!

12. Functional and Effective Connectivity

13. Models of effective connectivity = system models.But what precisely is a system? change ofstate vectorin time • 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 overall system staterepresented by state variables change ofstate vectorin time

14. Practical steps of a DCM study - I • Conventional SPM analysis (subject-specific) • DCMs are fitted separately for each session → consider concatenation of sessions or adequate 2nd level analysis • 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? • Defining criteria for inference: • single-subject analysis: stat. threshold? contrast? • group analysis: which 2nd-level model?

15. Attention to motion in the visual system Stimuli250 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 only A - motion stimuli with attention (detect changes) N - motion stimuli without attention S - no motion PPC V3A V5+ Attention – No attention Büchel & Friston 1997, Cereb. Cortex Büchel et al.1998, Brain

16. SPC V1 IFG V5 A simple DCM of the visual system Attention • Visual inputs drive V1, activity then spreads to hierarchically arranged visual areas. • Motion modulates the strength of the V1→V5 forward connection. • The intrinsic connection V1→V5 is insignificant in the absence of motion (a21=-0.05). • Attention increases the backward-connections IFG→SPC and SPC→V5. 0.55 0.26 0.72 0.37 0.56 0.42 Motion 0.66 0.88 -0.05 Photic 0.48 Re-analysis of data fromFriston et al., NeuroImage 2003

17. SPC SPC V1 V1 V5 V5 Comparison of three simple models Model 1:attentional modulationof V1→V5 Model 2:attentional modulationof SPC→V5 Model 3:attentional modulationof V1→V5 and SPC→V5 Attention Attention Photic Photic Photic SPC 0.55 0.03 0.85 0.86 0.85 0.70 0.75 0.70 0.84 1.36 1.42 1.36 0.89 0.85 V1 -0.02 -0.02 -0.02 0.56 0.57 0.57 V5 Motion Motion Motion 0.23 0.23 Attention Attention 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

27. 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.

28. Stimulators and Coils Single-pulse TMS Repetitive TMS Paired-pulse TMS

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

30. TMS and PET Frameless Stereotaxy • 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.

31. Paired-pulse TMS/ [15O] H2OPET 12 ms ISI 3 ms ISI t t 3.6 3.8 3.0 3.0 Z= 61 Z= 48 Strafella and Paus, J. Neurophysiol. 2001

32. TMS and PET [11C] raclopride ROI Analysis • 7.4% reduction in ipsilateral caudate • No change in contralateral caudate • No change in putamen, accumbens dorsolateral PFC occipital t -6 X X -3 Cortical Control of Dopamine release Reductions in [11C]raclopride BP Strafella et al., J. Neurosci. 2001

33. TMS and PET [11C] raclopride ROI Analysis • 9.4% reduction in ipsilateral putamen • No change in contralateral putamen • No change in caudate, accumbens occipital Primary motor cortex t -6 X X -3 Cortical Control of Dopamine release Reductions in [11C]raclopride BP Strafella et al., Brain 2003

34. IRMf et TMS ‘offline’ Continuous Theta Burst Stimulation (cTBS) • 80% active motor threshold • Similar to slow rTMS • Suppresses the cortico-excitability • Long lasting after-effect Huang et al. Neuron 2005

35. IRMf et TMS online

36. Acknowledgements SPM DCM course Drs. Marcus Gray & Petra Vetter Drs. Klaas Enno Stephan &Lee Harrison Dr. Randy McInstosh, Dr. Barry Hortwitz TMS/PET Dr. Antonio P. Strafella, Ji-Hyun Ko