1. MEG fundamentals

2. Magnetoencephalography (MEG)

3. Typical MEG responses to auditory stimuli

4. The signals: single neuron
A single active neuron is not sufficient. ~100,000 simultaneously active neurons are needed to generate a measurable M/EEG signal.
Pyramidal cells are the main direct neuronal sources of EEG & MEG signals.
Synaptic currents but not action potentials generate EEG/MEG signals (AP bidirection, cancel, and time constant is small, cannot accumulate.)
pyramidal cell bodies (somas) average around ~ 20μm

5. The signals: group of neurons
dipole
Layer 4 Pyramidal cells
5-10nAm
Cumulative post-synaptic currents
of ~100,000 pyramidal neurons

6. The signals: reception
Volume currents
Magnetic field
Electrical potential difference (EEG)
cortex
skull
scalp
MEG pick-up coil

7. The signals: orientation
External magnetic field produced by a radial source in a perfectly spherical conductor
cannot be picked up by MEG sensors.

8. Take home message 1
MEG/EEG: measuring signals due to aggregate post-synaptic currents (modeled as dipoles)
Lead fields are the predicted signal produced by a dipole of unit amplitude.
MEG is limited by SNR. Higher SNR= resolution of deeper structures.
EEG is limited by head models. More accurate head models= more accurate reconstruction.

9. The equipment: MEG
- 269 °C
Sensors
(Pick up coil)
SQUIDs

10. The equipment: SQUIDS measuring extremely weak magnetic signals
SQUID: superconducting quantum interference device
It is an ultrasensitive detector of magnetic flux.
It is made up of a superconducting ring interrupted by one or two Josephson Junctions.
Can measure field changes of the order of 10^-15 (femto) Tesla
(compare to the earth’s field of 10^-4 Tesla)

11. The equipment: sensors flux transfers
Magnetometers: measure the magnetic flux through a single coil
Gradiometers: measures the gradient (numerical rate of change) of magnetic flux through multiple coils

12. Sensitivity of different types of sensors
Magnetometer: pick up all magnetic activities
Gradiometer: more sensitive to the sources nearby
Another factor to be considered is the detection coil configuration. Conceptually, the easiest input circuit to consider for detecting changes in magnetic fields is a pure magnetometer (Fig. 2). However, magnetometers are extremely sensitive to all magnetic signals in the environment. This may be acceptable if one is measuring ambient fields. However, if the magnetic signal of interest is weak, then environmental magnetic interference may prevent measurements. If the signal source is close to the detection coil, then a gradiometer coil may allow a weak signal to be measured. Figure 3 shows the relative noise rejection for 1st and 2nd derivative gradiometers.  The figure insert shows a first order gradiometer, consisting of two coils connected in series but wound in opposite senses, and separated by a distance "b", called the gradiometer baseline.  A uniform magnetic field (e.g., from a distant environmental source) would couple equal but opposite quantities of flux into the two coils, resulting in zero net flux in the gradiometer, or zero signal.  However, signal sources that are close to the lower coil (relative to the baseline, or separation between coils) would couple significantly more flux into the lower coil than into the upper coil; this would result in a net flux in the gradiometer and hence the signal would be detected.

13. Dipole location reflecting in different types of sensors

14. Measures and sensor types
Waveforms
Topographies
Axial Gradiometers
Planar Gradiometers
Raw
ERP

15. Take home message 2
SQUIDs with gradiometers: picking up extremely weak magnetic signals
Our MEG system: axial gradiometer (dipole patterns)

16. Data analysis
Signal-to-Noise ratio (SNR)

17. Extracting signals from noise
Average: eliminating random noise

18. Emergence of the ERP

19. Temporal domain The ERP: Amplitude and Latency

20. Temporal domain: components
Temporal components:
distinct topographies at specific latencies
Auditory M100

21. Spectral domain: different speed of oscillation

22. Spectral domain: ways to separate different oscillation
Filtering: selecting different frequencies
A ‘sieve’ method

23. Spectral domain: filtering for particular frequency bands

24. Spectral domain: filtering for particular frequency bands

25. Spectral domain: ways to separate different oscillation
Fourier Transform (FFT): simultaneously separating into different frequencies

26. FFT for MEG signals
Raw
Low-pass filtered
cutoff frequency at 30 HZ

27. Spectral domain: components
Spectral components:
distinct topographies in specific frequencies
Characteristic Bands
Frequency
Range
Correlates (?)
Delta 
2 – 4 Hz
sleep
Theta 
4 – 7 Hz
memory
Alpha 1
8 – 10 Hz
sensory, attention
(more A) 2
10 – 12 Hz
(more V)
Beta 1
12 – 18 Hz ? 2
18 – 25 Hz
Mu/motor
Gamma
Phase-locked 
25 – 35 Hz
Updating, transient memory
Induced 
35 – 80 Hz
Cognitive binding
Litvak et al., 2010 Hz
Finger movement (power)
Hearing sentences (Phase)
Luo and Poeppel, 2007

28. Take home message 3 Average trials to increase SNR
Temporal (and spectral) analysis: latency, magnitude, and topography
Components indicating cognitive functions

29. MEG is a tool, you need ideas
Cognitive neuroscience: neural computation mediating cognitive functions
What questions do you want to ask? (hypothesis, theory?)
Can these questions be answered by the selected tool? (experimental design? prediction? measures and dependent variables (learned today)?)
We will learn more from Dr. Poeppel

31. Reliability and ERPs Individual differences The bad: The good:
Lower SNR (Grade average to further increase SNR)
Hard to infer to population
The good:
Correlates to individual behaviors to infer cognitive functions

32. induced vs.phase-locked responses BOTH are stimulus-driven
induced rhythm vs. locked response
Will be canceled if opposite
Solution: make them all positive – take power

33. Epoching

34. Signal Averaging

35. Characteristic Auditory Evoked Related Potentials

36. •power spectrum •bandpass filtering

37. The Evoked Gamma Band Response

38. Evoked GBR Properties Total Duration: ~100 ms Cycle Duration: ~ 25 ms
Spectral Energy Range: ~25-75 Hz
Latency: ~ 50 ms
Amplitude: < 1 uV
Generator Source: Cortex (MEG Studies)
Generated by stimulus onset

39. (conductivity & geometry)
The forward problem
MEG
Lead fields
EEG
Head tissues
(conductivity & geometry)
Dipolar sources

40. Different head models (lead field definitions) for the forward problem
Finite Element
Boundary Element
Multiple Spheres
Single Sphere
Simpler
models

41. Summary
EEG is sensitive to deep (and radial) sources but a very precise head model is required to get an accurate picture of current flow.
MEG is relatively insensitive to deeper sources but forward model is simple.

42. Lead fields Forward problem MEG EEG forward model Lead fields
Dipolar sources

43. The inverse problem Y = g()+ 
The inverse problem (estimating source activity from sensor data)
is ill-posed. So you have add some prior assumptions
Y = g()+ 
MEG
forward
model
EEG
For example,
can make a good guess
at realistic orientation
(along pyrammidal cell bodies,
perpendicular to cortex)