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Functional brain Imaging : from measurement to cognition Line Garnero Laboratoire de Neurosciences Cognitives & Imagerie Cérébrale CNRS UPR640 Centre de.

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Presentation on theme: "Functional brain Imaging : from measurement to cognition Line Garnero Laboratoire de Neurosciences Cognitives & Imagerie Cérébrale CNRS UPR640 Centre de."— Presentation transcript:

1 Functional brain Imaging : from measurement to cognition Line Garnero Laboratoire de Neurosciences Cognitives & Imagerie Cérébrale CNRS UPR640 Centre de Magnétoencéphalographie Hôpital La Salpêtrière

2 Brain Imaging Principle Brain in action Rely « brain state « with a behaviour (motor, cognitive on normal and pathological subjects) Non invasive : available to human brain Large scale observation (10 6 neurones) Properties

3 Required Models - Observable quantities for activation : neurobiological and hemodynamical model -Recording devices : Physical model : from biology to physics -Experimental protocols : Psychological / cognitive models of processing - Data Analysis Brain processing model -Interpretation : Based on both models

4 SUMMARY -Neurophysiological bases -Recording devices and physical models -Exprimental protocols -Data analysis : segregation or integration -«Brain Reading » and Brain Computer Interface

5 Two imaging modalities Electrical neuronal activity Non invasive Imaging High temporal resolution Problem for localisation EEG/MEG Hemodynamical activity Non invasive Spatial resolution ~1mm Limited temporal resolution Functional MRI

6 Neurobiological principles

7 Bases physiologiques de l I. C. F. Metabolisme ATP synthesis Consumption of Glucose et O2 EEG - MEG PPSE - PPSI Intracellular ou extracellular Hemodynamics Deoxygenation Blood Flow Increase TEP IRMf Neuronal Action Potential Daprès B. Mazoyer

8 Recordings principles Instrumentation

9 Recorded currents Quelques mm2 Conduction currents (extracellular) Current dipole intracellular Cortical Macrocolumn neurones Q =I x d ~10 à 100 nAm I

10 Physical modelisation of the EM fields Js : source current Jc : conduction current Jc = E = - grad(V) tissue conductivities div(Js +Jc) = 0 ====> div(Js) = div[ grad(V)] Biot et Savart Law Maxwell equations in quasistatic approximation (PPSE : 10 ms)

11 Electroencephalography (EEG) et Magnétoencéphalography (MEG) : MEG : magnetic field measurement. Scale : tesla SQUID sensors 1st MEG : 1969 EEG : Electric Potential measurements. Electrodes on the head surface Scale : few microvolts 1er EEG : 1929

12 MEG INSTRUMENTATION Detection : Flux detection : coils + Flux transformer : squids SQUIDS : Low Temperature Supra Josephson Junctions

13 MEG EEG

14 functional MRI : principle Capillaires i i e B e B e B e B i B i BOLD effect n n Oxyhémoglobine : diamagnetic. n n Désoxyhémoglobine : paramagnétique. local variation of magnetic susceptibility due to the variation of concentration of désoxyhémoglobine (intrisic contrast agent) variation of the RMN signal amplitude

15 Hemodynamical response n Response to a stimulus : slow variation (15 s). Average on many repetitions Functional MRI : principle

16 Outside B Inside B M Resonance Radiofrequency B1 B1B1 B0B0 Time relaxations Bloch equations MRI Physical principle Proton spins

17 IRM screenprojecteur PC plaque à filtre MRI acquisition temps FMRI sequence

18 Data : Structural and functional MRI IRMf signal IRMf IRMa Transversal relaxation time T2* Longitudinal relaxation time T1

19 Experimental protocols

20 Objectives –Reveals cerebral activation linked to a mental process (cognitive, sensori-moteur...) Test the activation of a specific area Localize the areas activated in a given process Find the dynamics of a mental process (chronometry, netwrk dynamics...) Localize the origin of the measured fields source reconstruction ? IRMf MEEG [N. George et A.L. Paradis]

21 Repetition of different tasks and stimuli Contrasts between tasks and conditions Study on group of subjects comparison between different groups Experimental protocols

22 Cognitive substraction Equalization of all required processes Except process of interest Hypothesis –The differential cerebral activity of the contrasts TEST - CONTROL reveal only the process of interest CONTROL Process Of interest CONTRÔLE _ = Process Of interest TEST constant task and variable stimulus constant stimulus and variable task keeping task and stimulus fixed and variable « internal state » [N. George et A.L. Paradis]

23 Data Analysis

24 Cerebral processing theories Functional segregation : spatial cluster of cells having a same functional role. Functional specialisation : a cortical area is specialized in a (sub-)processing of one (several) function(s). Functional integration : transitory cooperation of several areas for the realization of one function Edelman et Tononi, 2001

25 Data anaysis principles for specialization Seggregation : Area localization Chronometry Sequential processing

26 MEG/EEG analysis : segregation Sequential processing : evoked potentials Dawson, 1951 repetition of stimulations and conditions reproducibility of neural events evoked by the condition (task+stimulus) and subject state

27 Interpretation : chronology Early latencies (<< 200 ms) Exogeneous waves processing in sensory areas or Depend on the physical properties of the stimulus Late latencies > ( 200 ms) Endogeneous waves associative and cognitive processing Depend on the task and subject state Nomenclature En EEG: Pxxx ou Nxxx, positif or negative potential peaking at culminant à xxx ms En MEG: Mxxx, magnetif field peaking at xxx ms N145 P100

28 Source localisation Reconstruction in time and space of neural sources at the origin of MEG and EEG surface signals J?J? Inverse problem ill posed problem focal or distributed source models Direct problem conductivity values of head tissues numerical resolution Capteur MEG B(t) V(t) Electrode EEG J(t)

29 Dipolar models Hypothesis Few areas are activated siumltaneously Focal activation modeled by one dipolar current thumb index middle little Example Hand finger somatotopy at 30 ms Right hand index Little finger

30 Distributed models Resolution Linear inverse problem Regularisation One image for each time sample Hypotheses No prior on the number of activated areas Distribution of sources normal to the cortical surface Estimation of sources amplitudes

31 ms descent P. Senot et al., in revision catch Senot et al Application : ball catching

32 Temporal series fMRI voxel time course Statistical image (SPM) amplitude time General Linear Model ÜFitting Üstatistical image fMRI data analysis J.B. Poline

33 n n Quantification of voxel activation by a model of the hemodynamic response (function gamma, de Poisson ou gaussienne). fMRI analysis principle 1 s 15 s ON OFF n n Convolution of the the time serie of the protocol with this function n n Test of significativity at each voxel (comparison between bold isgnals and convoluted function) n Individual and group statistics

34 Gaze direction Head orientation directaverted straight oblique fMRI : example 4 experimental conditions : Daprès N. George

35 All conditions versus rest IRM fonctionnelle :results Fusiform gyrus area : direct versus averted

36 Cooperation : Network characterization : Interaction between areas Cerebral connectivity Parallel processing Data anaysis principles for cooperation

37 Oscillations in MEG/EEG Hypotheses Transient neuronal assemblies (Varela et al, 2001) Any cognitive process corresponds to the emergency of a neuronal assemby distributed, specific, transient and synchronous Local oscillatory activities Local signs of loop activation Time frequency analysis Long distance synchrony Interaction between areas (from sensors or sources) Coherence, phase synchronisation

38 b Puissance émise ( ) a Frequence (Hz) Perception Temps (ms) FREQUENCE PERCEPTIONNO PERCEPTION ms ms synchronieDe-synchronyAbsence of synchrony Rodriguez et al, Nature, 1999 Gamma band : 30 – 60 Hz

39 Networks imaging – Inverse problem + dynamical analysis of synchronies Cortical sources Dynamic links

40 t …...…… Rivalité Binoculaire Cosmelli et al

41 Functional integration Analyses of inter-regional effects : fMRI analysis for cooperation From SPM course Functional connectivity = the temporal correlation between spatially remote neurophysiological events Functional connectivity = the temporal correlation between spatially remote neurophysiological events MODEL-free 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 MODEL-dependent

42 Defined by the correlation of the BOLD signal between regions A and B Area A Area B Ensemble de régions Functional connectivity From H. Benali

43 R-Cereb Cx L-Cereb Cx Ant-Cereb L-Visual A. R-Visual A. R-Pre-Cu L-Pre-Cu L-Par Cx R-Par Cx R-M1 L-M1 SMA R-PM L-PM R-DLPFC L-DLPFC Cing L-Put R-Put L-Thal R-Thal L-CaudN R-CaudN r 0 1 Example Motor network From H. Benali

44 Effective connectivity : Dynamical Causal Modelling Friston et al., NeuroImage, 2003; David et al., NeuroImage, 2006 Neuronal variables: –Synaptic time constant –Synaptic efficacy –Inhibition/Excitation –Connectivity (networks) Macroscopic data at the brain level: –Local field potentials –Scalp EEG/MEG –Functional MRI Forward problem Given the generative model, one can predict the measured data Inverse problem Given the measured data, one can estimate the generative model From O. David

45 DCM : forward models ERP/ERF EEG/MEG Spatial forward model g data y Dynamics f Input u parameters θ states x data y Ballon model x(t) Bold signal From O. David

46 SI SII input Forward Backward Lateral (100%) 2.67 (100%) 3.57 (99%) 0.95 (53%) DCM : Somatosensory Evoked Potential From O. David

47 Limits of Brain Imaging Large scale observation (1 million neurons) Correlation between behavior and brain images no causality (necessary condition) Reveal only images linked to the observed task difficult generalisation, multiple parameters Brain : dynamical system : do not forget TIME !!!!!!!!!


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