The relationship between different brain areas is characterized by functional networks through correlations of time series obtained from neuroimaging experiments. Due to its high spatial resolution, functional MRI data is commonly used for generating functional networks of the entire brain. These networks are comprised of the measurement points/channels as nodes and links are established if there is a correlation in the measured time series of these nodes. However, since the evaluation of correlation becomes more accurate with the length of the underlying time series, we construct in this paper functional networks from MEG data, which has a much higher time resolution than fMRI. We study in particular how the network topologies change in an experiment on ambiguity of words, where the subject first receives a priming word before being presented with an ambiguous or unambiguous target word.

We produced a double-layer thin-film heater to detrap magnetic flux in a SQUID sensor. The heater is integrated on a sensor chip, and consists of a lower resistor layer and an upper superconducting layer to cancel the magnetic field produced by the heater current. The SQUID sensor is cooled below its critical temperature with a temperature gradient to detrap the flux completely. To make the gradient, we had to decrease heater power to zero over an interval exceeding 10-4 second in our experiment, which is almost equal to the sensor chip's thermal time constant. The integrated heater effectively controls the temperature profile and detraps flux in the sensor.