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Functional brain Imaging : from measurement to cognition

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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 Properties 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 (106 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.
Neuronal Action Potential Metabolisme ATP synthesis Consumption of Glucose et O2 PPSE - PPSI Intracellular ou extracellular TEP EEG - MEG Hemodynamics Deoxygenation Blood Flow Increase TEP IRMf D’après B. Mazoyer

8 Recordings principles
Instrumentation

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

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

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

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

13 MEG EEG

14 BOLD effect functional MRI : principle Oxyhémoglobine : diamagnetic.
Désoxyhémoglobine : paramagnétique. Capillaires c i e B 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
Functional MRI : principle Hemodynamical response Response to a stimulus : slow variation (15 s). Average on many repetitions

16 MRI Physical principle
Proton spins Inside B M Outside B Time relaxations Bloch equations Resonance Radiofrequency B1 B1 B0

17 MRI acquisition FMRI sequence IRM screen projecteur PC plaque à filtre
temps FMRI sequence PC plaque à filtre

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

19 Experimental protocols

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

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 _ = 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
Dawson, 1951 repetition of stimulations and conditions reproducibility of neural events evoked by the condition (task+stimulus) and subject state Sequential processing : evoked potentials

27 Endogeneous waves Exogeneous waves Interpretation : chronology
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 Late latencies > ( 200 ms) Endogeneous waves associative and cognitive processing Depend on the task and subject state Early latencies (<< 200 ms) Exogeneous waves processing in sensory areas or Depend on the physical properties of the stimulus

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

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

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

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

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

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

34 fMRI : example  4 experimental conditions : Gaze direction direct
averted straight Head orientation oblique D’après N. George

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

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

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 Gamma band : 30 – 60 Hz PERCEPTION NO PERCEPTION synchronie
12 10 8 6 4 400 800 -400 b 60 40 20 Puissance émise ( s ) a Frequence (Hz) Perception Temps (ms) FREQUENCE ms ms ms ms synchronie De-synchrony Absence of synchrony Rodriguez et al, Nature, 1999

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

40 Rivalité Binoculaire t .. .… Cosmelli et al

41 Functional integration
fMRI analysis for cooperation Functional integration Analyses of inter-regional effects : 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 MODEL-dependent From SPM course

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

43 Example Motor network r 1 From H. Benali 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 1 From H. Benali

44 Effective connectivity : Dynamical Causal Modelling
Forward problem Given the generative model, one can predict the measured data 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 Inverse problem Given the measured data, one can estimate the generative model From O. David Friston et al., NeuroImage, 2003; David et al., NeuroImage, 2006

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

46 DCM : Somatosensory Evoked Potential
3.57 (99%) SII SII 0.95 (53%) Forward Backward Lateral 27.68 (100%) 2.67 (100%) SI input 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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