| Summary: | 碩士 === 國立交通大學 === 多媒體工程研究所 === 97 === It has been widely accepted that neurons in the human brain collectively have synchronous patterns of activities. The past findings have suggested that temporal correlation may relate to the communications between the distributed areas. There are some studies in magnetoencephalography and electroencephalography that analyze the functional connectivity between cortical areas using the oscillatory features of neuronal activity. However, temporal dynamics of neuronal activities is generally consisted of cross-frequency components. Therefore, it is also important to directly investigate the functional connectivity as well as general synchronization.
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 information exchange. The method can identify the correlation distribution referred to a specified position, called the reference region. In principle, we can apply our method on all pairs of grid points to identify all possible neural networks of correlated activities.
Our method exploits a maximum-correlation criterion that maximizes the significant level of correlation between the reference region and the entire brain volume. 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 determine the correlated regions. Different from the conventional source localization method, we focus on the areas which have the similar temporal patterns with the reference signal. We demonstrated the applicability of the proposed method on real data. In the mirror neuron experiment, most of the regions we revealed are reported by the previous findings 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 dynamics 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 correlated regions at each time point and thus reveal the dynamic behavior of the neural network.
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