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EEG Topography and Tomography (sLORETA) in Analysis of Abnormal Brain Region for Mild Depression

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Brain Informatics and Health (BIH 2016)

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Abstract

Mild depression disorder affects a large number of people, and if not correctly treated, it could evolve into major depression disorder. So far, lots of studies have suggested that several abnormal brain regions are linked to major depression disorder, including prefrontal cortex (PFC), anterior cingulate cortex (ACC) etc. However mild depression disorder gained less attention. This study therefore examined whether mild depression exists dsyregulation in these brain regions. 20 subjects participated in our experiment, and affective facial expressions pictures were used as stimuli when recording EEG signals. Brain activity of theta and alpha bands were examined using EEG topography and standardized low-resolution electromagnetic tomography (sLORETA). Results suggested that mild depressed subjects exhibited higher activity in motor cortex (Brodmann area: 4, 6) and lower activity in visual cortex (Brodmann area: 18, 19) and prefrontal cortex (Brodmann area: 11, 47) compared to normal controls. These finding indicated that mild depressed subjects paid much more attention to negative facial expressions. Future studies should be focused on the role of brain regions played in mild depression in more detail.

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Acknowledgements

This work was supported by the National Basic Research Program of China (973 Program) (No. 2014CB744600), the Program of International S&T Cooperation of MOST (No. 2013DFA11140), the National Natural Science Foundation of China (grant No. 61210010, No. 61300231).

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Correspondence to Bin Hu .

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Li, X., Cao, T., Hu, B., Sun, S., Li, J. (2016). EEG Topography and Tomography (sLORETA) in Analysis of Abnormal Brain Region for Mild Depression. In: Ascoli, G., Hawrylycz, M., Ali, H., Khazanchi, D., Shi, Y. (eds) Brain Informatics and Health. BIH 2016. Lecture Notes in Computer Science(), vol 9919. Springer, Cham. https://doi.org/10.1007/978-3-319-47103-7_30

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  • DOI: https://doi.org/10.1007/978-3-319-47103-7_30

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  • Publisher Name: Springer, Cham

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  • Online ISBN: 978-3-319-47103-7

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