[1] Luo Guoping, Liu Gang, Zhao Jing, Yao Zhijian, et al. Depression recognition using functional connectivitybased on dynamic causal model [J]. Journal of Southeast University (English Edition), 2011, 27 (4): 367-369. [doi:10.3969/j.issn.1003-7985.2011.04.004]

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Depression recognition using functional connectivitybased on dynamic causal model()

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Journal of Southeast University (English Edition)[ISSN:1003-7985/CN:32-1325/N]

Volumn:

27

Issue:

2011 4

Page:

367-369

Research Field:

Biological Science and Medical Engineering

Publishing date:

2011-12-31

Info

Title:

Depression recognition using functional connectivitybased on dynamic causal model

Author(s):

Luo Guoping¹;  Liu Gang¹;  Zhao Jing¹;  Yao Zhijian²;  Lu Qing¹

¹ Research Center for Learning Science, Southeast University, Nanjing 210096, China
² Nanjing Brain Hospital Affiliated to Nanjing Medical University, Nanjing 210029, China

Keywords:

depression recognition;  fMRI;  dynamic causal model;  Bayesian model selection

PACS:

Q64;TP310.4

DOI:

10.3969/j.issn.1003-7985.2011.04.004

Abstract:

Dynamic casual modeling of functional magnetic resonance imaging(fMRI)signals is employed to explore critical emotional neurocircuitry under sad stimuli. The intrinsic model of emotional loops is built on the basis of Papez’s circuit and related prior knowledge, and then three modulatory connection models are established. In these models, stimuli are placed at different points, which represents they affect the neural activities between brain regions, and these activities are modulated in different ways. Then, the optimal model is selected by Bayesian model comparison. From group analysis, patients’ intrinsic and modulatory connections from the anterior cingulate cortex(ACC)to the right inferior frontal gyrus(rIFG)are significantly higher than those of the control group. Then the functional connection parameters of the model are selected as classifier features. The classification accuracy rate from the support vector machine(SVM)classifier is 80.73%, which, to some extent, validates the effectiveness of the regional connectivity parameters for depression recognition and provides a new approach for the clinical diagnosis of depression.

References:

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Memo

Memo:

Biographies: Luo Guoping(1987—), male, graduate; Yao Zhijian(corresponding author), male, doctor, associate professor, yaozhijian@yahoo.cn.
Foundation item: The National Natural Science Foundation of China(No.30900356, 81071135).
Citation: Luo Guoping, Liu Gang, Zhao Jing, et al. Depression recognition using functional connectivity based on dynamic causal model[J].Journal of Southeast University(English Edition), 2011, 27(4):367-369.[doi:10.3969/j.issn.1003-7985.2011.04.004]

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