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

2. 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

3. 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

4. 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

5. Intra-Cranial Sources Papanicolaou 1998:31 Dipole (source current)

6. 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)

7. 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)

8. 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

9. 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)

10. Magnetic flux from source currents Source current Magnetic flux Magnetometer

11. Recording of Magnetic Signals

12. An MRI Machine

13. 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

14. 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

15. 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

16. 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

17. 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

18. 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

19. Surface distribution of magnetic signals Extrema

20. 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

21. Co-registration of MEG and MRI space

22. MEG scan co-registered with MRI scan using fiducial markers

23. Result of co-registration

24. 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

25. 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

26. 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

27. 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

28. 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

29. 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

30. 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

31. 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)

32. 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

33. Some MEG/MSI Findings

34. Speech recognition: MEG results Hemispheric AsymmetryWernicke's Area

35. Variability in location of Wernicke’s area (different subjects) From MEG lab, UT Houston

36. Wernicke’s area in bilinguals From MEG lab, UT Houston

37. 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.

38. 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

39. 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.

40. 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!

41. 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

42. An MEG study from Max Planck Institute Naming animals from visual (picture) input LH RH

43. 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.

44. Imaging methods compared

45. 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

46. 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

47. 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

48. 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

49. 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

50. Neuronal Structure and Function (Pulverműller 2002, Chapter 2)

51. 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

52. 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

53. Gray matter and white matter Grey matter White matter

54. Three views of the gray matter Different stains show different features

55. Computers and Brains: Different Structures, Different Skills  Computers Exact, literal Rapid calculation Rapid sorting Rapid searching Faultless memory Do what they are told Predictable  Brains Flexible, fault tolerant Slow processing Association Intuition Adaptability, plasticity Self-driven activity Unpredictable Self-driven learning

56. 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

57. Global neurotransmitters Released into interneural space, has global effect – e.g. serotonin, dopamine

58. 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

59. 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

60. 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

61. Connecting fibers of pyramidal neurons Apical dendrite Basal dendrites Axon

62. Interconnections of pyramidal neurons Input from distant cells Input from neighboring columns Output to distant cells

63. Neuronal Structure and Function: Connectivity  Synapses of a typical pyramidal neuron: Incoming (afferent) – 50,000 (5 x 10 4 ) Outgoing (efferent) – 50,000  Number of synapses in cortex: 28 billion neurons (Mountcastle’s estimate)  i.e., 28 x 10 9  Synapses in the cortex (do the math) 5 x 10 4 x 28 x 10 9 = 140 x 10 13 = 1.4 x 10 15 Approximately 1,400,000,000,000,000 i.e., over 1 quadrillion

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