Coupling of regional activations in a human brain during an object and face affect recognition task.

Magnetic field tomography (MFT) was used to extract estimates for distributed source activity from average and single trial MEG signals recorded while subjects identified objects (including faces) and facial expressions of emotion. Regions of interest (ROIs) were automatically identified from the MFT solutions of the average signal for each subject. For one subject the entire set of MFT estimates obtained from unaveraged data was also used to compute simultaneous time series for the single trial activity in different ROIs. Three pairs of homologous areas in each hemisphere were selected for further analysis: posterior calcarine sulcus (PCS), fusiform gyrus (FM), and the amygdaloid complex (AM). Mutual information (MI) between each pair of the areas was computed from all single trial time series and contrasted for different tasks (object or emotion recognition) and categories within each task. The MI analysis shows that through feed-forward and feedback linkages, the "computation" load associated with the task of identifying objects and emotions is spread across both space (different ROIs and hemispheres) and time (different latencies and delays in couplings between areas)-well within 200 ms, different objects separate first in the right hemisphere PCS and FG coupling while different emotions separate in the right hemisphere FG and AM coupling, particularly at latencies after 200 ms.

Temporal correlations versus noise in the correlation matrix formalism: an example of the brain auditory response.

We adopt the concept of the correlation matrix to study correlations among sequences of time-extended events occurring repeatedly at consecutive time intervals. As an application we analyze the magnetoencephalography recordings obtained from the human auditory cortex in the epoch mode during the delivery of sound stimuli to the left or right ear. We look into statistical properties and the eigenvalue spectrum of the correlation matrix C calculated for signals corresponding to different trials and originating from the same or opposite hemispheres. The spectrum of C largely agrees with the universal properties of the Gaussian orthogonal ensemble of random matrices, with deviations characterized by eigenvectors with high eigenvalues. The properties of these eigenvectors and eigenvalues provide an elegant and powerful way of quantifying the degree of the underlying collectivity during well-defined latency intervals with respect to stimulus onset. We also extend this analysis to study the time-lagged interhemispheric correlations, as a computationally less demanding alternative to other methods such as mutual information.