Transcript Ling 411 – 10 Functional Brain Imaging (cont’d) MEG REVIEW Functional Brain Imaging Techniques     Electroencephalography (EEG) Positron Emission Tomography (PET) Functional Magnetic Resonance Imaging (fMRI) Magnetoencephalography (MEG) •

Ling 411 – 10
Functional Brain Imaging (cont’d)
MEG
REVIEW
Functional Brain Imaging Techniques
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Electroencephalography (EEG)
Positron Emission Tomography (PET)
Functional Magnetic Resonance Imaging (fMRI)
Magnetoencephalography (MEG)
• Magnetic source imaging (MSI)
 Combines MEG with MRI
Magnetoencephalography (MEG)
 MEG (MagnetoEncephaloGraphy)
measures the magnetic field
around the head
 Compare EEG: Measures voltage
changes on the scalp
 MSI (Magnetic Source Imaging) is
MEG coupled to MRI
Magnetoencephalography
magnetic
brain
picture
production of
Magnetoencephalography (MEG)
 MEG (MagnetoEncephaloGraphy) measures the
magnetic field around the head
 Compare EEG: Measures voltage changes on the scalp
 MSI (Magnetic Source Imaging) is MEG coupled to MRI
Intra-Cranial Sources
Dipole (source
current)
Papanicolaou 1998:31
How MEG works
 Records the magnetic flux or the magnetic fields
that arise from the source current
 A current is always associated with a magnetic
field perpendicular to its direction
 Magnetic flux lines are not distorted as they pass
through the brain tissue because biological tissues
offer practically no resistance to them (cf. EEG)
Magnetoencephalography (MEG)
 Records the magnetic flux or the magnetic fields that arise
from the source current
 A current is always associated with a magnetic field
perpendicular to its direction
 Magnetic flux lines are not distorted as they pass through
the brain tissue because biological tissues offer practically
no resistance to them (cf. EEG)
A dipole is a small current source
 Dipole generates a magnetic field
 Dendritic current from apical dendrites of
pyramidal neurons
 At least 10,000 neighboring neurons firing
“simultaneously” for MEG to detect
Recording of the Magnetic Flux
 Recorded by special sensors called magnetometers
 A magnetometer is a loop of wire placed parallel to
the head surface
 The strength (density) of the magnetic flux at a
certain point determines the strength of the
current produced in the magnetometer
 If a number of magnetometers are placed at
regular intervals across the head surface, the shape
of the entire distribution by a brain activity source
can be determined (in theory)
Magnetic flux from source currents
Magnetometer
Magnetic flux
Source current
Recording of Magnetic Signals
An MRI Machine
Recording of the Magnetic Flux
 Present day machines have 248 magnetometers
 The magnetic fields that reach the head surface are
extremely small
 Approximately one million times weaker than the
ambient magnetic field of the earth
 Because the magnetic fields are extremely small, the
magnetometers must be superconductive (have
extremely low resistance)
 Resistance in wires is lowered when the wires are
cooled to extremely low temperatures
Recording of the Magnetic Flux
 When the temperature of the wires approaches absolute
zero, the wires become superconductive
 The magnetometer wires are housed in a thermally insulated
drum (dewar) filled with liquid helium
 The liquid helium keeps the wires at a temperature of about
4 degrees Kelvin
 The magnetometers are superconductive at this temperature
Recording of the Magnetic Flux
 The currents produced in the magnetometers are also
extremely weak and must be amplified
 Superconductive Quantum Interference Devices (SQUIDS)
 The magnetometers and their SQUIDS are kept in a dewar,
which is filled with liquid helium to keep them at an
extremely low temperature
How a MEG Recording is Made
 The MEG machine is
located in a magnetically
shielded room
• Subjects cannot wear any
metal because it affects the
recording
 Digitization process
 After digitization, the task
is run and the recording is
made
The Digitization Process
 Needed for co-registration with MRI
• MRI scan is done later
• Provides images
• MSI – Magnetic Source Imaging
 Method
• 5 points
 3 electrodes on forehead
 2 earpieces
• Subjects must remain extremely still during
the digitization process
 After digitization, the task is run and the
recording is made
Dipolar Distribution of the Magnetic Flux
 In the following figure, one set of concentric circles
represents the magnetic flux exiting the head and
the other represents the re-entering flux
 This is called a dipolar distribution
 The two points where the recorded flux has the
highest value are called extrema
 The flux density diminishes progressively, forming
iso-field contours
Surface distribution of magnetic signals
Extrema
Dipolar Distribution of the Magnetic Flux
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From the dipolar distributions, we can determine some
characteristics of the source
The source is below the mid-point between the extrema
(points where recorded flux has highest value)
The source is at a depth proportional to the distance between
the extrema
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Extrema that are close together indicate a source close to the
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surface of the brain
A source deeper in the brain produces extrema that are further
apart
The source’s strength is reflected in the intensity of the
recorded flux
The orientation of the extrema on the head surface indicates
the orientation of the source
Co-registration of MEG and MRI space
MEG scan co-registered with MRI scan
using fiducial markers
Result of co-registration
Event-related brain responses: EEG & MEG
 Both types of signals come from the same type of event:
active dipoles
• Different directions from the dipoles
• Detected by different devices
 With EEG
• ERP – event-related potential
 With MEG
• ERF – event-related (magnetic) field
• Addition from 100 or more trials for each tested
condition needed to get measurable data
The inverse problem
 A problem for EEG and MEG
 Locating the dipole(s) based on signals reaching surface of
scalp
 Problem: Multiple solutions are possible
• Cf. solving x + y = 24
 Computer uses iterative procedure to come up with best
fit
 The problem is compounded by the fact that the brain is a
parallel processor
• Many dipoles at each temporal sampling point
MSI before neurosurgery
 MSI is preferred because mapping by cortical
stimulation increases the patients’ susceptibility to
infections as a result of lengthened surgery durations
 MSI can be performed prior to the scheduled surgery so
that the surgeons can plan the best way to remove the
damaged area while avoiding language areas as best
they can
Temporal Resolution of MEG
 Excellent – unlike fMRI and PET
 The temporal order of activation of areas in a pattern can
be discerned
 The time course of the activation can be followed
 MEG has potential to detect the activation of several brain
regions as they become active from moment to moment
during a complex function such as recognition
Temporal Resolution of MEG
 Only with MEG can we detect the activation of several
brain regions as they become active from moment to
moment during a complex function such as recognition
 But it is (at present state of the art) virtually impossible
to achieve precision
Time course of activation
 We can follow the activation of a source across time
 The magnetic fields recorded in MEG are evoked
 Activation at each point in time is recorded (millisecond
sensitivity)
 Sources of early components of Evoked Fields circumscribe
the modality-specific sensory areas
 Sources of late components circumscribe different sets of
brain regions (mostly association cortex)
• These activation patterns are function- (or task-) specific
Spatial limitation of MEG
 Magnetic flux is perpendicular to direction of electrical
current flow
 Flux is therefore relatively easy to detect if dendrites are
parallel to surface of skull
• i.e., for pyramidal neurons along the sides of sulci
 But hard or impossible to detect if vertical
• i.e., for pyramidal neurons at tops of gyri or at bottoms
of sulci
The challenge of MSI
 The cortex is a parallel processor
• Hundreds or thousands of dipoles can be active
simultaneously
 Multiple dipoles make comprehensive inverse dipole
modeling virtually impossible
 Hence, compromises are necessary
• Sample larger time spans (up to 500 ms)
• Sample larger areas (up to several sq cm)
Other limitations of MEG and EEG
 Problem: orientation of dipoles
 For MEG
• Activity in some areas is practically undetectable
 Dipoles at tops of gyri
 Dipoles at bottoms of sulci
 For EEG
• Dipoles on sides of sulci are hard to detect
Some MEG/MSI Findings
Speech recognition: MEG results
Hemispheric Asymmetry
Wernicke's Area
Variability in location of Wernicke’s area
(different subjects)
From MEG lab, UT Houston
Wernicke’s area in bilinguals
From MEG lab, UT Houston
Localization of phonemes:
The claim of Obleser et al.
 Different locations (in temporal
lobe) for different vowels
 The anterior-posterior axis
corresponds to the backness of
a vowel – the more back the
vowel, the more posterior the
source location
 The superior-inferior axis
corresponds to the height of a
vowel (inverse relationship) –
the higher the vowel, the more
inferior the source location of
that vowel
From: Ladefoged, P. (2001). Vowels and Consonants:
An Introduction to the Sounds of Languages. Malden,
Massachusetts: Blackwell Publishers, Inc.
Distinguishing features of vowels
 Tongue height corresponds
to F1 (first formant)
 Front-back dimension
corresponds to F2 (2nd)
 The formants are detected in
auditory processing (upper
temporal lobe)
 Tongue positions are
controlled by motor cortex
(frontal lobe) and monitored
in parietal lobe
Tongue positions
From: Ladefoged, P. (2001). Vowels and Consonants:
An Introduction to the Sounds of Languages. Malden,
Massachusetts: Blackwell Publishers, Inc.
MEG and localization of phonemes
 Wernicke’s area may be
organized phonemotopically
 The anterior-posterior axis
corresponds to the backness of
a vowel – the more back the
vowel, the more posterior the
source location
 The superior-inferior axis
corresponds to the height of a
vowel (inverse relationship) –
the higher the vowel, the more
inferior the source location of
that vowel
From: Ladefoged, P. (2001). Vowels and Consonants: An
Introduction to the Sounds of Languages. Malden,
MEG and localization of phonemes
 Results: The relative positions of neural representations
for vowels in Wernicke’s area correlate with the relative
positions of the vowels in articulatory space
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Obleser, Elbert, Lahiri, & Eulitz, 2003
Obleser, Lahiri, & Eulitz, 2004
Obleser, Elbert, & Eulitz, 2004
Eulitz, Obleser, & Lahiri, 2004
 Can this finding be replicated?
• Finding supported by different lab!
• Shestakova, Brattico, Soloviev, Klucharec, & Huotilainen, 2004!
Shestakova et al. experiment (2004)
 Done in Helsinki, Russian vowels [i a u]
• Obleser et al. in Germany, German vowels [i a u]
 Results similar to those of Obleser et al.
• Higher cortical location for [a]
• Front-back cortical location corresponds to articulatory
positions
 They go two steps further:
• Input from different speakers (all male)
• Similar findings in both LH and RH
An MEG study from Max Planck Institute
Naming animals from visual (picture) input
LH
RH
More information on MEG
 The University of Texas Health Science Center at Houston
Division of Clinical Neurosciences MEG Lab:
• http://www.uth.tmc.edu/clinicalneuro/
 Papanicolaou, A. (1998). Fundamentals of Functional Brain
Imaging: A Guide to the Methods and their Applications to
Psychology and Behavioral Neuroscience.Lisse: Swets &
Zeitlinger.
Imaging methods compared
A practical consideration: Cost
 Most expensive: MEG
• About $2 million for the machine
• $1 million for magnetically shielded
room
 Next most expensive: PET
 Next: fMRI
 Cheapest: EEG
Temporal resolution – summary
 PET: 40 seconds and up
 fMRI: 10 seconds or more
 MEG and EEG: instantaneous
• Theoretically it is possible to do ms by ms tracking, to
follow time course of activation
• Commonly used sampling rate for MEG: 4 ms
• Practically, such tracking is difficult or impossible
 The inverse problem
 Too many dipoles at each point in time
Spatial Resolution
 EEG: Poor
 PET: Fair – 4-5 mm
 fMRI: Fair – 4-5 mm
• MRI: Good – 1 mm or less
 MEG: Fairly good – 3-4 mm or less
• Under good conditions
Sensitivity of Imaging Methods
 All of the methods have limited sensitivity
 MEG
• 10,000 dendrites in close proximity have to be active
to detect signal
 PET and fMRI
• Similar limitations
 Any activation that involves fewer numbers goes
undetected
Other limitations of MEG and EEG
 Problem: orientation of dipoles
 For MEG
• Activity in some areas is practically undetectable
 Dipoles at tops of gyri
 Dipoles at bottoms of sulci
 For EEG
• Dipoles on sides of sulci are hard to detect
Neuronal Structure and Function
(Pulverműller 2002, Chapter 2)
Neuronal Structure and Function I
The Cortex is a Network
 Pulvermüller (2002):
• The brain is not like a computer
“…any hardware computer configuration can realize
almost any computer program or piece of software.”
“… it may be that the neuronal structures themselves
teach us about aspects of the computational
processes that are laid down in these structures.”
 Connectivity as key property
Since it is a network,
The cortex operates by means of connections
 Grey matter
• The functional units are not individual
neurons but clusters of neurons
 Cortical columns (cf. next slide)
• Horizontal connections to and from
neighboring columns
 Excitatory
 Inhibitory
 White matter
• Connections between distant columns
 Excitatory only
Gray matter and white matter
Grey
matter
White
matter
Three views of the gray matter
Different stains
show different
features
Computers and Brains:
Different Structures, Different Skills
 Computers
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Exact, literal
Rapid calculation
Rapid sorting
Rapid searching
Faultless memory
Do what they are told
Predictable
 Brains
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Flexible, fault tolerant
Slow processing
Association
Intuition
Adaptability, plasticity
Self-driven activity
Unpredictable
Self-driven learning
Things that brains but not computers can do
 Acquire information to varying degrees
• “Entrenchment”
• How does it work?
 Variable connection strength
 Connections get stronger with repeated use
 Perform at varying skill levels
• Degrees of alertness, attentiveness
• Variation in reaction time
• Mechanisms:
 Global neurotransmitters (next slide)
 Variation in blood flow
 Variation in available nutrients
 Presence or absence of fatigue
 Presence or absence of intoxication
Global neurotransmitters
Released
into
interneural
space, has
global effect
– e.g.
serotonin,
dopamine
Neuronal Structure and Function:
Connectivity
 White matter: it’s all connections
• Far more voluminous than gray matter
• Cortico-cortical connections
 The fibers are axons of pyramidal neurons
 They are all excitatory
• White since the fibers are coated with myelin
 Myelin: glial cells
 There are also grey matter connections
• Unmyelinated
• Local
• Horizontal, through gray matter
• Excitatory and inhibitory
Pyramidal neurons and their connections
 Connecting fibers
• Dendrites (input): length 2mm or less
• Axons (output): length up to 10 cm
 Synapses
• Afferent synapses: up to 50,000
 From distant and nearby sources
• Distant – to apical dendrite
• Local – to basal dendrites or cell body
• Efferent synapses: up to 50,000
 On distant and nearby destinations
• Distant – main axon, through white matter
• Local – collateral axons, through gray matter
Proportion of pyramidal cells in the cortex
 Abeles (1991: 52) says 70%
 Mountcastle says 70% - 80% (1998: 54)
• Based on information from Feldman (1984)
 Pulvermüller (2002: 13) says 85%
• Based on information from Braitenburg & Schüz (1998)
 Some difference comes from how spiny stellate cells are
counted
• Pyramidal or not?
 No discrete boundary between these categories
Connecting fibers
of pyramidal
neurons
Apical dendrite
Basal dendrites
Axon
Interconnections of pyramidal neurons
Input from
distant cells
Input from
neighboring
columns
Output to
distant cells
Neuronal Structure and Function:
Connectivity
 Synapses of a typical pyramidal neuron:
• Incoming (afferent) – 50,000 (5 x 104)
• Outgoing (efferent) – 50,000
 Number of synapses in cortex:
• 28 billion neurons (Mountcastle’s estimate)
 i.e., 28 x 109
 Synapses in the cortex (do the math)
• 5 x 104 x 28 x 109 = 140 x 1013 = 1.4 x 1015
• Approximately 1,400,000,000,000,000
• i.e., over 1 quadrillion
end