| dc.description.abstract | For opening the black box of human, understanding the brain, many tools have been applied to help people probe the questions of brain. Functional magnetic resonance imaging (fMRI) and Electroencephalography (EEG) are of the most extensive application. fMRI has very high spatial resolution which not only helps us understand tissue of cortex, but also provides us information of the functional sites of brain in cognitive experiment. On the other hand, EEG has very high temporal resolution; it helps us to enable us to see the direct action of brain (neurons) and the sequential processing of brain on cognitive operation. However, the neural signals are very weak and usually present complex property in these tools. We need a better method to get correct and precise brain action on processing singles.
We propose a visualization map for the detection of activated regions in fMRI and of the located source in EEG. Via the visual examination of the map, the user can easily identify activated regions in the fMRI and the located source in EEG. The proposed visualization map fully utilizes the property of the spatial connectivity of activated pixels which is a key factor on determining the significance of activated regions in cortex. The advantage of this technique is people can identify regions of activation without any priori knowledge. Several datasets have also been employed to verify the broad applicability of the technique. Receiver operating characteristic (ROC) analysis has been used to evaluate the performance under many conditions. Results show that with certain contrast-to-noise ratios, visualization map can detect the functional activation in fMRI and source location in EEG.
In fMRI, the robustness and effectiveness of the V map are demonstrated by the simulation results on an artificial data set and a real life one. Comparing to model-based methods, our V map can effectively detect regions of activation in fMRI without a priori knowledge of expected hemodynamic responses. Comparing with data-driven-based methods, the V map offers several appealing properties: 1) the processing time involved with clustering algorithms is no longer needed; 2) the determination of the optimal cluster number is not necessary; 3) the order of data and the random initialization of the cluster centers are not necessary; 4) the user no longer needs to determine which cluster really corresponds to the activated regions.
In EEG, we also use an artificial data set and a real life one to demonstrate the robustness and effectiveness of the V map. Comparing to dipole-model method, V map can efficiently detect location of sources in EEG without a priori assumption on the number of dipoles in the brain. Comparing with statistical nonparametric mapping method, V map offers several appealing properties: 1) the processing time is no longer needed; 2) one condition is sufficient to define sources location; 3) the number of samples does not influence the reliability of result; 4) correcting ratio of V map is very low in the different SNR.
In addition, the V map is capable of detecting regions within which pixels have similar activation patterns which may be different to the real model function. If these un-expected activation regions can be further inspected more information may be revealed. | en_US |