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| [[BR]] To start with, if you come from fMRI background, there are few important differences between the haemodynamic imaging (fMRI, PET) and neurophysiological imaging (MEG, EEG) approaches, all closely related to each other: | <<BR>> To start with, if you come from fMRI background, there are few important differences between the haemodynamic imaging (fMRI, PET) and neurophysiological imaging (MEG, EEG) - read about them on [[MEG_vs_fMRI]] page. |
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| [[BR]] 1. Timing aspect. In MEG/EEG, you can follow brain activation with millisecond precision. Using the CBU machine, you will typically sample your MEG signal with 1kHz sampling rate, which will enable you to have a snapshot of the brain activation every millisecond at all 306 sensors. | <<BR>> Other things you may want to consider: |
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| [[BR]] MEG activations, as a direct reflection or neuronal activity, last considerably less time than BOLD, typically only a few hundred milliseconds. | == Spatial resolution issues == <<BR>> MEG's sensitivity to deep sources is low: magnetic field decays rapidly with distance. If you expect your activations to come mostly from subcortical structures, think twice before attempting to study them in MEG. If you expect a complex pattern in which both subcortical and cortical activation overlap in time, telling them apart will not be trivial and may not be possible at all. There are ways to confront these problems, usually involving some prior assumptions about to the activation loci, which can be theory driven or based on data from other modality. <<BR>> MEG's resolution is not uniform along the brain surface either. Sources tangential to the surface are picked up much better than those radial (perfectly radial sources cannot be recorded at all, but perfect radial sources probably do not exist or at least rare due to the brain's shape). <<BR>> Close-by sources tend to blur into a single activation spot in MEG data, especially if they have similar orientation. Don't expect to be able to tell apart sources closer to each other than 1cm. |
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| [[BR]] During this short time, the activation is usually not uniform and has complex dynamics. [[BR]] 2. As the result of the above, time-locking of your stimuli to the MEG data becomes very important. Here, the major methodological difference with fMRI is that '' you have to send triggers from your stimulation equipment to the MEG acquisition machine and record them with the data''. These can later be used for time-locking brain responses to the stimuli. Unlike the fMRI experiments, the MEG machine will not send any pulses or triggers to the stimulation computer. [[BR]] 3. The activation pattern is not known beforehands, and you will not be fitting your data to any function (cf. HRF convolution in fMRI). You will, instead, record the acivation patterns as they appear in the recording and simply analyse their parameters (amplitude, latency, topography) numerically (after a number of preprocessing steps, of course.) |
== Stimulation issues == <<BR>>As previously discussed, MEG activation are fast and relatively short. Keep this in mind when putting your stimuli together. Typically, a simple stimulus (say short sound or picture) will cause changes in the MEG waveform starting as soon as 50ms or earlier. The response will have complex dynamics and will be over in 2-3 hundred milliseconds. Stick a few stimuli together and all these will overlap making your analysis much more difficult. <<BR>> For the same reasons, it is good to have a silent (or constant) baseline, i.e. to allow the neurons to return to the basic state before the nest stimulus is presented. In practice, it means allowing a few hundred millisecond of stimulus-free time before presenting the next stimulus. <<BR>> Typically, ERF design requires tens or hundreds of stimulus repetitions for a good SNR. ERFs from single trials will then be averaged together to yield a measurable responses. |
Designing MEG studies
To start with, if you come from fMRI background, there are few important differences between the haemodynamic imaging (fMRI, PET) and neurophysiological imaging (MEG, EEG) - read about them on MEG_vs_fMRI page.
Other things you may want to consider:
Spatial resolution issues
MEG's sensitivity to deep sources is low: magnetic field decays rapidly with distance. If you expect your activations to come mostly from subcortical structures, think twice before attempting to study them in MEG. If you expect a complex pattern in which both subcortical and cortical activation overlap in time, telling them apart will not be trivial and may not be possible at all. There are ways to confront these problems, usually involving some prior assumptions about to the activation loci, which can be theory driven or based on data from other modality.
MEG's resolution is not uniform along the brain surface either. Sources tangential to the surface are picked up much better than those radial (perfectly radial sources cannot be recorded at all, but perfect radial sources probably do not exist or at least rare due to the brain's shape).
Close-by sources tend to blur into a single activation spot in MEG data, especially if they have similar orientation. Don't expect to be able to tell apart sources closer to each other than 1cm.
Stimulation issues
As previously discussed, MEG activation are fast and relatively short. Keep this in mind when putting your stimuli together. Typically, a simple stimulus (say short sound or picture) will cause changes in the MEG waveform starting as soon as 50ms or earlier. The response will have complex dynamics and will be over in 2-3 hundred milliseconds. Stick a few stimuli together and all these will overlap making your analysis much more difficult.
For the same reasons, it is good to have a silent (or constant) baseline, i.e. to allow the neurons to return to the basic state before the nest stimulus is presented. In practice, it means allowing a few hundred millisecond of stimulus-free time before presenting the next stimulus.
Typically, ERF design requires tens or hundreds of stimulus repetitions for a good SNR. ERFs from single trials will then be averaged together to yield a measurable responses.