MEG covariance difference analysis: a method to extract target source activities by using task and control measurements

The concept of 'developing the brain': a new natural science for learning and education

From the viewpoint of biology, learning and education can be defined as the processes of forming neuronal connections in response to external environmental stimuli, and of controlling or adding appropriate stimuli, respectively. Learning and education can thus be studied as a new field of natural sciences with the entire human life span as its subject, thus including various problems such as fetal environment, childcare, language acquisition, general/special education, and rehabilitation. Non-invasive imaging of higher-order brain functions in humans will clarify the brain's developmental processes, and will provide various evidence for learning sciences. This new approach is called 'developing the brain' or 'brain science and education'. The origin of the concept and its present state are described and its future prospects are briefly analyzed.

Paired MEG data set source localization using recursively applied and projected (RAP) MUSIC

An important class of experiments in functional brain mapping involves collecting pairs of data corresponding to separate "Task" and "Control" conditions. The data are then analyzed to determine what activity occurs during the Task experiment but not in the Control. Here we describe a new method for processing paired magnetoencephalographic (MEG) data sets using our recursively applied and projected multiple signal classification (RAP-MUSIC) algorithm. In this method the signal subspace of the Task data is projected against the orthogonal complement of the Control data signal subspace to obtain a subspace which describes spatial activity unique to the Task. A RAP-MUSIC localization search is then performed on this projected data to localize the sources which are active in the Task but not in the Control data. In addition to dipolar sources, effective blocking of more complex sources, e.g., multiple synchronously activated dipoles or synchronously activated distributed source activity, is possible since these topographies are well-described by the Control data signal subspace. Unlike previously published methods, the proposed method is shown to be effective in situations where the time series associated with Control and Task activity possess significant cross correlation. The method also allows for straightforward determination of the estimated time series of the localized target sources. A multiepoch MEG simulation and a phantom experiment are presented to demonstrate the ability of this method to successfully identify sources and their time series in the Task data.