Functional Brain Imaging (cont’d) MEG Ling 411 – 10

Functional Brain Imaging Techniques  Electroencephalography (EEG)  Positron Emission Tomography (PET)  Functional Magnetic Resonance Imaging (fMRI)  Magnetoencephalography (MEG) Magnetic source imaging (MSI)  Combines MEG with MRI REVIEW

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 Papanicolaou 1998:31 Dipole (source current)

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 Source current Magnetic flux Magnetometer

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  From the dipolar distributions, we can determine some characteristics of the source 1.The source is below the mid-point between the extrema (points where recorded flux has highest value) 2.The source is at a depth proportional to the distance between the extrema Extrema that are close together indicate a source close to the surface of the brain A source deeper in the brain produces extrema that are further apart 3.The source’s strength is reflected in the intensity of the recorded flux 4.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

Testing Reliability of MSI  Necessary in early stages of research Does MEG give reliable localization results?  Compare with results of Wada test Excellent correlations found (But this tests only very crude localization)  Compare with results of intraoperative mapping MSI and mapping by cortical stimulation demonstrate similar localization abilities – excellent correlation

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)

Some MEG/MSI Findings

Speech recognition: MEG results Hemispheric AsymmetryWernicke'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 From: Ladefoged, P. (2001). Vowels and Consonants: An Introduction to the Sounds of Languages. Malden, Massachusetts: Blackwell Publishers, Inc. Tongue positions

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, Massachusetts: Blackwell Publishers, Inc.

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 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:  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

The Territory of Neurolinguistics: An Intellectual Territory with three dimensions

Dimension 1: Size Dimension 2: Static – Dynamic nm μm mm - - cm - - m a j o r s t r u c t u r a l c h a n g e s m a l l s t r u c t u r a l c h a n g e f u n c t i o n / o p e r a t i o n a n a t o m i c a l s t r u c t u r e s DynamicDynamic Static

Tiny sizes – nm to μm range Synaptic Structure

Small sizes – μm range  Pyramidal cell Diameter of cell body: μm Diameter of axon: up to 10 μm Diameter of apical dendrite: up to 10 μm  Cortical minicolumn Diameter: μm  Layers of the cortex Average thickness of one layer: 500 μm

Middle range – the Cortex From top to bottom, about 3 mm

Representation, Processing, Change (the second dimension)  Static Representation of linguistic information  Large scale ( LARGE-SCALE REPRESENTATION )  Small scale ( SMALL-SCALE REPRESENTATION )  Dynamic Linguistic information processing ( PROCESSING ) Learning and adapting ( CHANGE OF STRUCTURE )

Understanding Representation  The large scale (sq cm and up) How organized? What components? Where located? How interconnected?  The middle scale (sq mm and below) Minicolumns Maxicolumns Clusters of columns Interconnections of columns Internal structure of minicolumns  The small scale Internal structure of neurons

Representation at the large scale  Principles of organization  Linguistic subsystems Broca’s area –  Phonological production  Syntax(?) Wernicke’s area – phonological recognition Conceptual areas Etc.  Interconnections of subsystems  Functional webs

What we know so far – Principles of Organization I “Wernicke’s Principle”  Each local area does a small job  Large jobs are done by multiple small areas working together, by means of interconnecting fiber bundles  The basic principle of connectionism  Consequences Distributed representation and local representation Distributed processing LARGE-SCALE REPRESENTATION

What we know so far – Principles of Organization II  Genetically determined primary areas Motor – frontal lobe Perceptual – posterior cortex  Somatic – parietal  Visual – occipital  Auditory – temporal  Hierarchy  Proximity  Plasticity LARGE-SCALE REPRESENTATION

The Proximity Principle  Neighboring areas for closely related functions The closer the function the closer the proximity  Intermediate areas for intermediate functions  Consequences Members of same category will be in same area Competitors will be neighbors in the same area LARGE-SCALE REPRESENTATION

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