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EEG and MEG: functional brain imaging with high temporal resolution Syed Ashrafulla
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EEG and MEG: Functional Brain Imaging with High Temporal ...EEG and MEG: functional brain imaging with high temporal resolution Syed Ashrafulla . electrical signals in the brain Source:

Jan 30, 2020

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  • EEG and MEG: functional brain imaging with high temporal resolution

    Syed Ashrafulla

  • electrical signals in the brain

    Source: Baillet, S., Mosher, J. C., & Leahy, R. M. (2001). Electromagnetic brain mapping. IEEE SIgnal Processing Magazine, (November).

  • recordable signals Pyramidal Neuron

    Soma

    Apical Dendrites

    Axon

    Right Hand Rule

    Source:Matti Hamalainen

    Murakami, S., & Okada, Y. (2006). Contributions of principal neocortical neurons to magnetoencephalography and electroencephalography signals. The Journal of physiology, 575(Pt 3), 925–36. doi:10.1113/jphysiol.2006.105379

  • measurable currents

    • We can only measure assemblies of neurons.

    0.2pAm

    10nAm Or 50,000 synchronous cells

    Weakest measurable cortical signal Model as one “dipole”

    Current dipole of cortical pyramidal cell

    Area 0.63mm2

    Cortical area

    0.9mm

  • electroencephalography (EEG)

    intracellu

    lar curren

    t extr

    acel

    lula

    r cu

    rren

    t

    extracellular cu

    rrent

  • magnetoencephalography (MEG)

    induced magnetic field

  • MEG signal strength

  • MEG artifacts

    • Use signal space projection and noise cancellation techniques in preprocessing.

  • what can/can’t be seen

    Source: Matti Hamalainen

  • what can/can’t be seen

    Source: Matthew Longo

  • sensitivity profiles

    EE

    G

    ME

    G axial grad

    iom

    eter M

    EG

    pla

    nar

    gra

    dio

    met

    er

    ME

    G m

    agn

    eto

    met

    er

  • EEG vs MEG

    EEG MEG Signal magnitude 10 mV (easy to detect) 10 fT, difficult to detect

    Measurement Secondary currents Primary currents

    Signal purity Skull/scalp attenuation Little effect of skull/scalp

    Temporal Resolution ~ 1 ms ~ 1 ms

    Spatial Localization ~ 1 cm < 1 cm

    Experimental Flexibility Moves with subject Stationary with subject

    Dipole Orientation Tangential & radial Most sources are not fully radial

    Only tangential

    Source: Matthew Longo

  • EEG/MEG vs. fMRI

    EEG/MEG fMRI Temporal Resolution ~ 1 ms ~ 1 s

    Signal Type Direct (currents) Indirect (BOLD)

    Signal Reconstruction Ill-posed inversion Deconvolution

    Spatial Localization ~ 1 cm ≅ 1 mm for high-T

    Sensitivity depth ~ 4 cm Whole-brain

    Sensitivity profile drops off as square of distance from sensor

    Signal orientation Tangential (and radial) Can cause signal cancellation

    Agnostic

  • resolution comparison

  • modelling EEG/MEG recordings

    1Am

    Or in matrix form:

    qam1

    m2

    m3m4

    m5

    m6

    m7

    g1

    g2

    g3g4

    g5

    g6

    g7

    m1 = g1 ´1

    m2 = g2 ´1

    m1 = g1a ´qa + g1

    b ´qb + g1c ´qc

    m2 = g2a ´qa + g2

    b ´qb + g2c ´qc

    qb

    qc

    m =

    m1

    m2

    é

    ë

    êêêê

    ù

    û

    úúúú

    =

    g1a g1

    b

    g2a g2

    b

    é

    ë

    êêêê

    ù

    û

    úúúú

    1

    0

    é

    ë

    êêê

    ù

    û

    úúú

    =G

    1

    0

    é

    ë

    êêê

    ù

    û

    úúú

    m =

    m1

    m2

    é

    ë

    êêêê

    ù

    û

    úúúú

    =

    g1a g1

    b

    g2a g2

    b

    é

    ë

    êêêê

    ù

    û

    úúúú

    qa

    qb

    é

    ë

    êêêê

    ù

    û

    úúúú

    +

    n1

    n2

    é

    ë

    êêêê

    ù

    û

    úúúú

    =Gq+ n

    Sensor (measurement) noise

  • inverse imaging

    s1

    s2

    sn

    Assume a density of dipoles oriented normally to the cortical surface. Find their amplitude.

    m =Gq+nÞ q̂ = argminq

    m-Gq2

    q̂ = HmÜ q̂a = HaTmÜ Ha = argmin

    h

    hTGa=1

    hT Cov m( )( )h

    Minimum-norm estimation (MNE):

    Minimum-variance beamforming (LCMV):

    (Hui2010, NeuroImage)

    (Hauk2004, NeuroImage)

  • time-frequency decompositions

    Ä

    X st

    =

    t

    f

    Cstf = X st *wtf

    θ band: 4-7Hz α band: 8-14Hz β band: 15-30Hz γ band: 30-100Hz

    s: spatial index t: temporal index f: frequency index Source: Dimitrios Pantazis

  • connectivity & EEG/MEG • Connectivity = Relation between x[n] and y[n] Correlation: do x[n] and y[n] change at the same time?

    Coherence: do x[n] and y[n] change at the same time for frequency f?

    Causality: Is y[n] required to generate x[n]?

    Granger causality: Does y[n-1] help predict x[n]?

    All methods benefit from the high temporal resolution of EEG/MEG.

  • BrainStorm demonstration