imitationobservationexecution leftrightleftrightleftright poteriorinsula dorsalstream ventralstream supermarginal superiorfrontal gyrus supermarginal gyrus medialprefrontal cortex
90 Discussion
Chapter 5
Conclusion
92 Conclusion
In this thesis, we have proposed a beamformer-based imaging method of correlated brain activities that can reveal the neural network with similar temporal patterns for in-formation exchange. The method can identify the regions correlated to a specified brain region, called the reference region. In principle, we can apply our method on all pairs of grid points to identify all possible the neural networks of correlated activities.
Our method exploits a maximum-correlation criterion that maximizes the significant level of correlation between the reference region and other regions inside the brain. The maximum correlation criterion helps to analytically and accurately determine the dipole orientation in a closed-form manner and thus determine the spatial filter very efficiently for each position. The correlation map can be calculated to reveal cortical regions with significant similarity to the reference position in the brain.
The experiments with simulation data demonstrated that our method can accurately de-termine the correlated region. Different from the conventional source localization method, we focus on the areas which have the similar temporal patterns with the reference signal. In the mirror neuron experiment, most of the regions we revealed are reported by the previous finding of emotional processing, face perception and the mirror neuron system. Moreover, we can provide the time information about when these regions are correlated to the neural network.
In summary, the proposed method can be used to directly study dynamic of correlation brain areas based on electromagnetic recordings of brain activities. Given the reference region as one of the areas in the neural network, our method can estimate the the correlated regions at each time point and thus reveal the dynamic behavior of the neural network.
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