Rotatory scanning for improved discrimination of very low frequency components of the magneto-encephalogram. Gardner-Medwin AR (1992). Biomagnetism '91: Clinical aspects. Ed. Hoke M., Erne S.N., Okada Y.C. & Romani G.L. Excerpta Medica: Elsevier, Amsterdam, pp.881-885
Rotatory scanning for improved discrimination of very low frequency
components of the magneto-encephalogram
Department of Physiology, University College London, London WC1E 6BT, UK
Clinical magneto-encephalography (MEG) signals that rise and fall
with a time course of minutes would be of great interest, because they
may arise from pathological ionic disturbances in the brain. Such signals
have been recorded from anaesthetised rabbits during the ionic disturbance
known as Spreading Depression . Similar signals may occur in man in
conditions such as concussion, stroke, and migraine, all of which involve
profound and slowly changing disturbances of local neurological function.
Direct evidence of ionic disturbances is not available from other non-invasive
techniques in these conditions.
Conventional MEG is poor at discriminating very slowly changing signals in the relevant frequency band: 0.001-0.1 Hz. Baseline stability can be poor, environmental noise is substantial, and screening is less effective and more expensive than at higher frequencies. Faster changing signals have been observed associated with Spreading Depression in vitro , but the conditions in vivo, and in clinical conditions, may tend to produce mainly the slowly changing signals . Some signals have been recorded in clinical conditions . Such data are not amenable to averaging to improve noise rejection, in the manner possible with experimental signals elicited by repeatable stimuli . It would therefore be of great benefit to improve the baseline stability and artefact rejection in low frequency studies to assist comparison between signals in clinical situations and experimental tissue.
Source movement is a well known strategy allowing measurement
of DC fields [4,5]. The technique described here imposes a continuous rotatory
scanning motion on the source or subject in a horizontal plane under the
magnetometer (Fig.1). This converts horizontal gradients of DC fields into
AC fields at the rotation frequency (typically 0.5-1 Hz). Noise levels
and interference at this frequency are much less than at the frequencies
of direct interest . DC or slowly changing sources produce an extremely
narrow frequency band of signals of interest, as shown with Fast Fourier
Transform (FFT) analysis of the signals with steady calibration sources
of different strengths (Fig. 2). This makes it possible to use special
data processing techniques to extract the low frequency information from
just this narrow band of the spectrum, thereby rejecting most of the noise
at other frequencies.
The technique is being developed using portable equipment, with the aim that it may be used, especially for clinical research, wherever there are the best combinations of patients and magnetometer facilities for particular projects.
Up to 14 channels of magnetometer data and 2 channels of position data (monitoring the rotatory scanning in 2 perpendicular directions) are fed to a Data Acquistion Board (Microstar DAP 2400) in a portable computer (Mesh LCD-386). A correlation is carried out on the DAP board between each channel of magnetic data and each position channel. Since the position signals normally vary sinusoidally, each correlation is equivalent to extraction of a Fourier component of the magnetic signal at the rotation frequency. The correlation procedure is preferable, because it does not require prior knowledge of the rotation frequency and is tolerant of frequency variations and irregularities in the motion.
Figure 1. Rotatory scanning. A baseplate is supported on plastic balls
and is driven by a motor at 3 m distance via a fibreglass rod and gears
and belts (not shown). The radius of movement is 10 mm at a frequency 0.5-1
Hz. Note that the source maintains constant orientation: it does not spin
around. All parts of the source and baseplate have the same amplitude of
Figure 2. FFT spectra of SQUID magnetic signals with a 24 mm sensor coil 15 mm above the centre of the circular scanning trajectory (10 mm radius, 0.9 Hz) of a calibration current dipole. Four spectra are shown with the indicated dipole strengths. A small magnetic signal at the rotation frequency is seen even with no current, due to magnetic contamination.
Substantial data compression is achieved on the DAP Board. For example, with 10 magnetometer channels, 4800 Bytes/s (17 MB/hr) of sampling data are reduced to typically only 66 Bytes/s (240 kB/hr) of output to the computer for display and storage. This includes all the essential information about the low frequencies: at the end of each rotation cycle, the computer is sent (for each channel) the mean directly measured field (in pT) and the two regression slopes (in pT/mm) representing the gradients of the field due to the moving sources, in the directions of the two position sensors.
DISTRIBUTION OF THE MEASURED AC FIELD
The AC field arises from horizontal gradients of the DC field.
To a first (linear) approximation, which is adequate for most practical
purposes, the AC field at the rotation frequency is proportional to the
spatial gradients of the DC field due to sources on the moving platform.
The DC field and the resulting AC field over a current dipole source are
shown in Fig. 3. The second graph can be regarded as the steepness (regardless
of direction) of the surface formed by the first graph.
Figure 3. a. The DC vertical field (Bz) on a horizontal plane at a height h above a current dipole of strength Q in the y direction. (Units: h for x,y and (?o/4?)Qh-2 for Bz). b. The peak-to-peak AC field as in (a), with horizontal rotatory scanning of radius a (<<h). (Units: (?o/2?)aQh-3). Graph (b) is 2a times the absolute magnitude of the gradient of (a).
The peak-to-peak amplitude of the AC signals is comparable to
the maximum amplitude of the DC signals. The ratio for a<<h is 5.2
a/h, and the peak AC and DC signals are equal for a/h=0.2 (e.g. 10mm radius,
50mm source distance).
The AC fields measured with this technique have three notable features, apart from their benefit in noise reduction:
1. The maximum signal occurs directly over a dipole source.
2. The phase of the maximum signal directly indicates the dipole orientation.
3. There is improved rejection of signals due to distant magnetic contamination on the subject. This is because the field gradients that are measured fall off more steeply with distance than the fields themselves. This effectively increases the differential order of the gradiometer.
Trial tests with calibration sources confirm the qualitative improvements
in noise rejection that can be achieved in low frequency studies. Fig.
4 shows the output regression signals due to a 0.04Hz calibration source,
switched on for part of the time. These signals are essentially unaffected
by interference that caused large shifts in the directly measured field
(top trace). The ratio of the two regression signals indicates the dipole
orientation. The X regression trace gives a clear measure of the dipole
timecourse. The presence of a dead rabbit in the first part of the record
produced only a small steady superimposed shift due to contamination. This
trial was carried out with active cancellation of the Earth's magnetic
field, but with no magnetic screening. The baseline stability of the regression
records (referred to the size of dipoles that are detectable) is similar
to that achieved with direct DC recording in a screened room , even
though the ambient levels of interference were much greater.
Figure 4. Detection of a calibration dipole (monitored in the bottom trace) with rotatory scanning. At the start of the record, a dead frozen rabbit was positioned with its head directly under the sensor coil. The dipole source was 30 mm below the coil and 25mm off axis (next to the rabbit's ear). In the middle of the record, the rabbit was removed without disturbing the source.
The data shown here were obtained in the laboratory of SJ Swithenby, with help from him and K Fiaschi. I am grateful also to CN Guy for help and advice. Funded by the Wellcome Trust.
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