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

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)

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

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

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

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)

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

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

PPI: interpretation Two possible interpretations of the PPI term: V1 Modulation of V1  V5 by attention Modulation of the impact of attention on V5 by V1. V1V5 V1 V5 attention V1 attention

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!

Functional and Effective Connectivity

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 overall system state represented by state variables change of state vector in time

Practical steps of a DCM study - I 1.Conventional SPM analysis (subject-specific) DCMs are fitted separately for each session → consider concatenation of sessions or adequate 2 nd 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 2 nd -level model?

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

V1IFGV5 SPC Motion Photic Attention Visual inputs drive V1, activity then spreads to hierarchically arranged visual areas.Visual inputs drive V1, activity then spreads to hierarchically arranged visual areas. Motion modulates the strength of the V1→V5 forward connection.Motion modulates the strength of the V1→V5 forward connection. The intrinsic connection V1→V5 is insignificant in the absence of motion (a 21 =- 0.05).The intrinsic connection V1→V5 is insignificant in the absence of motion (a 21 =- 0.05). Attention increases the backward-connections IFG→SPC and SPC→V5.Attention increases the backward-connections IFG→SPC and SPC→V5. A simple DCM of the visual system Re-analysis of data from Friston et al., NeuroImage 2003

V1 V5 SPC Motion Photic Attention V1V5SPC Motion Photic Attention V1 V5SPC Motion Photic Attention Attention 0.23 Model 1: attentional modulation of V1→V5 Model 2: attentional modulation of SPC→V5 Model 3: attentional modulation of 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

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.

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

TMS coil TMS and Functional Imaging (PET) [ 15 O] H 2 O and [ 11 C]raclopride

Frameless Stereotaxy TMS 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.

3 ms ISI12 ms ISI t t Z= 61 Z= 48 Paired-pulse TMS/ [ 15 O] H 2 O PET Strafella and Paus, J. Neurophysiol. 2001

-3 -6 t Reductions in [ 11 C]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 X dorsolateral PFC X occipital TMS and PET [ 11 C] raclopride

-3 -6 t 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 [ 11 C] raclopride Cortical Control of Dopamine release

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

IRMf et TMS online

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