MAGNETOENCEPHALOGRAPHY Principles and Applications

BE Lab Group 2

OUTLINE

• Hardware Principles

• Data Acquisition

• Comparison & Applications

HARDWARE PRINCIPLES

SUPERCONDUCTING QUANTUM INTERFERENCE DEVICE (SQUID)

• Brain’s magnetic fields < 10-12 Tesla

• Lack of resistance increases sensitivity

• Extreme low temperature with liquid helium (≈ 4K)

• SQUID measures magnetic fields as small as 1 femtotesla

MEG HELMET

• The superconducting lead shell

• Gray mesh: shell’s contour

• Blue surface: SQUID sensor

• The shell helps shield the underlying SQUID array from ambient magnetic fields and also reflects the brain’s magnetic fields back to the SQUID

MAGNETICALLY SHIELDED ROOM

• Remove extraneous magnetic fields

MAGNETIC SOURCE IMAGING

• Using simple computer methods, information from MEG and MRI are integrated to form magnetic source localisation images

• Provide information about both the structure and function of the brain

• Extremely high temporal and spatial resolution

DATA ACQUISITION

MAGNETIC PRINCIPLES• The primary current is highly correlated with the

postsynaptic activity of brain neurones• The primary current generates

a potential distribution (EEG) and the associated volume currents

• The primary and volume currents together also create a magnetic field (MEG)

DIRECTION OF SIGNAL FLOW

SPHERICAL HEAD MODEL V.S. REALISTIC MODEL

• Spherical head model

• Concentric, homogeneous and isotropic

• Realistic Model

• Subject pre-scanned with MRI, locating brain structures such as blood vessels, bones and nerve fibres

• In boundary element method (BEM) a brain prototype specific to the subject is constructed, the non-spherical border of which is used as boundary conditions of the model; still assuming homogeneity and isotropy

MATHEMATICAL DERIVATION•

•

•

B r( ) = µ04π

J ′r( )∫ × r − ′rr − ′r 3 d ′v

J ′r( ) = J p ′r( )+ JV ′r( ) = J p ′r( )+σ ′r( )E ′r( )= J p ′r( )−σ ′r( )∇ ′r( )

B r( ) = B0 r( )+ µ04π

σ i −σ j( )ij∑ V ′r( )

Sij∫ × r − ′rr − ′r 3 d ′Sij

B0 r( ) = µ04π

J P ′r( )Sij∫ × r − ′r

r − ′r 3 d ′r

— Primary and Secondary currents

— Biot-Savart Law

— Solvable only when is knownJ p ′r( )V ′r( )

INVERSE PROBLEM

• Even for spherical model the inverse transform is highly compl ica ted ; add i t iona l experimental conditions make calculation even more difficult

• The preceding equations describe principles of forward transform; inverse transform is generally achieved with iterative methods

(Ferree et. al., 2000; Goncalves et. al., 2000)

COMPARISON & APPLICATIONS

BRAIN IMAGINGMODALITIES

Structural Imaging Functional Imaging

Computerised Tomography (CT)

Magneto/Electro-encephalography(MEG/EEG)

Magnetic Resonance Imaging (MRI)

Functional Magnetic Resonance Imaging (FMRI)

Positron Emission Tomograpy (PET)

Positron Emission Tomograpy (PET)

FEATURES OF MEG• Direct measure of brain function

• Very high temporal resolution (milliseconds)

• Excellent spatial resolution

• Completely non-invasive

• Complementary to other modalities, each adds to full picture

APPLICATIONS OF MEG

• Clinical applications

• Diagnosis of neuronal diseases

• Surgical planning (e.g. before epilepsy surgery)

• Research purposes

• Brain and cognitive sciences

COMPARISONS

is only part of the story: ever-changing connections are being made that can alter in a fractionof a second.

1.1.1 Accuracy in space and time

Figure 5.1 shows today’s methods and their accuracy in space and time. (See Chapter 1 forthe spatial and time magnitudes of the brain.) Techniques like functional magnetic resonanceimaging (fMRI), which records metabolic changes like blood oxygenation, have good spatialresolution and relatively poor time resolution. fMRI has a response time of about six secondsbecause the fMRI signal (called the BOLD signal) reflects a flow of oxygen-rich blood travel-ing to “hot spots” that are doing extrawork. The changes in blood flow take several seconds tocatch upwith the neuronal activity. fMRI is therefore too slow for tracking single neurons andpopulations in “real time.”

We do not have a complete census of all the neurons in the brain the way a human societymight conduct a census of the whole population. We are always sampling from a very largeset of active neurons. For that reason we cannot be sure that we know every single cell type inthe brain, down to the smallest level. Brain anatomists are constantly discovering new spe-cialized neurons in some local neighborhood. For example, the light receptors that adjustour body to sunlight and darkness were only discovered in recent years.

fMRI has very good spatial specificity compared to electroencephalography (EEG) andmagnetoencephalography (MEG), which use electrical and magnetic signals, respectively.

METHODS FOR OBSERVING THE LIVING BRAIN

Brain EEG & MEG

Opticaldyes

Multi-unitrecording

Single unit

Patch clampLight

microscopy

Microlesions2-D

eoxyglucose

TMS FMRI PET LesionMap

Column

Layer

Neuron

Dendrite

Synapse

Millisecond Second MinuteLog time

Log size

Hour Day

FIGURE 5.1 How good are current methods? Pros and cons of imaging techniques: differing imaging modalitieshave different resolutions. While some approaches have a very high temporal (time-based) resolution but a low spa-tial (space-based) resolution, other modalities have an opposite relation.

112 5. BRAIN IMAGING

(Baars & Gage, 2012)

MEG V.S. EEGMEG EEG

Signal magnitude 10 fT, difficult 10 mV, detect easy

Signal purity Little attenuation Skull/scalp attenuation

Temporal Resolution ≈ 1 ms ≈ 1 ms

Spatial Resolution < 1 cm ≈ 1 cm

Experimental Flexibility Stationary Moves with subject

Signal orientation Only tangential Tangential & radial

MEG V.S. FMRIEEG/MEG fMRI

Temporal Resolution ≈ 1 ms ≈ 1 s

Spatial Resolution ≈ 1 cm ≈ 1 mm

Signal Type Direct Indirect (BOLD)

Signal Reconstruction Ill-posed inversion Deconvolution

Sensitivity≈ 4 cm (Drops off as

square of distance)Whole-brain

Signal orientation Tangential only Agnostic

永齡⽣生醫⼯工程中⼼心

(Chen, 2013)

1F - MRI & MEG(Chen, 2013)

THANK YOUQuestions and Comments Welcomed

REFERENCES• Baars, B., & Gage, N. M. (2012). Fundamentals of Cognitive Neuroscience: A

Beginner's Guide: Elsevier Science.

• Basic Principles of Magnetoencephalography (http://web.mit.edu/kitmitmeg/whatis.html)

• Chen, J.-H. (2013). ⽣生醫影像：⽣生技醫療產業的⽣生⼒力軍.

• Goncalves, J.C. de Munck, R.M. Heethaar, F.H. Lopes da Silva, and van B.W. Dijk, “The application of electrical impedance tomography to reduce systematic errors in the EEG inverse problem—A simulation study,” Physiol. Meas., vol. 21, pp. 379-393, 2000.

• T.C. e, K.J. Eriksen, and D.M. Tucker, “Regional head tissue conductivity estimation for improved EEG analysis,” IEEE Trans. Biomed. Eng., vol. 47, pp. 1584-1592, 2000.

• Lindquist, M. (2014). Statistical Analysis of fMRI Data | Coursera. from https://class.coursera.org/fmri-001/lecture

• Martinos.org. (2014). Retrieved 19 April 2014, from http://www.martinos.org/neurorecovery/technology.htm

• M. Hämäläinen, R. Hari, R. Ilmoniemi, J. Knuutila, and O. Lounasmaa, “Magnetoencephalography. Theory, instrumentation and applications to the noninvasive study of human brain function,” Rev. Mod. Phys., vol. 65, pp. 413-497, 1993.

• Pauling, L., & Coryell, C. D. (1936). The magnetic properties and s t r u c t u r e o f h e m o g l o b i n , o x y h e m o g l o b i n a n d carbonmonoxyhemoglobin. Proceedings of the National Academy of Sciences of the United States of America, 22(4), 210.

• Poldrack, R. A., Mumford, J. A., & Nichols, T. E. (2011). Handbook of functional MRI data analysis: Cambridge University Press.

• SQUID Magnetometry: Harnessing the Power of Tiny Magnetic Fields by Steve Curtin (http://consciousresonance.net/?p=1716)

• What is MEG? (http://web.mit.edu/kitmitmeg/whatis.html)

• What is Magnetic Source Imaging (MSI)? (http://uuhsc.utah.edu/uumsi/what-is-msi.html)