Localizing brain interactions from rhythmic EEG/MEG data.

The interpretation of MEG/EEG data in terms of brain connectivity is largely obscured by artefacts of volume conduction, i.e. by the fact that a single source is observable in many channels. Here, we analyze a measure which is insensitive to spurious connectivity arising from volume conducted "self-interaction". For rhythmic data such a measure can be given by the imaginary part of the cross-spectrum between EEG/MEG channels. For the derivation we essentially exploit that a signal is not time-lagged to itself. To localize the sources of this observed interaction we fit a model cross-spectrum consisting of N interacting dipoles to the sample cross-spectrum. The relation to the maximum likelihood estimator will be discussed in detail. The method is illustrated for MEG data of human alpha rhythm in eyes closed condition. The eigenvalues of the imaginary cross-spectrum clearly indicate the presence of at least 4 necessarily interacting sources. Fits of 2 to 6 dipoles in a realistic volume conductor all resulted in locations scattered in the mesial part of the occipital lobe.

Dynamic cortical activity in the human brain reveals motor equivalence.

That animals and humans can accomplish the same goal using different effectors and different goals using the same effectors attests to the remarkable flexibility of the central nervous system. This phenomenon has been termed 'motor equivalence', an example being the writing of a name with a pencil held between the toes or teeth. The idea of motor equivalence has reappeared because single-cell studies in monkeys have shown that parameters of voluntary movement (such as direction) may be specified in the brain, relegating muscle activation to spinal interneuronal systems. Using a novel experimental paradigms and a full-head SQUID (for superconducting quantum interference device) array to record magnetic fields corresponding to ongoing brain activity, we demonstrate: (1), a robust relationship between time-dependent activity in sensorimotor cortex and movement velocity, independent of explicit task requirements; and (2) neural activations that are specific to task demands alone. It appears, therefore, that signatures of motor equivalence in humans may be found in dynamic patterns of cortical activity.