Your browser doesn't support javascript.
loading
Brain network analysis of adolescent depression with attempted suicide / 中国医学影像技术
Chinese Journal of Medical Imaging Technology ; (12): 981-985, 2020.
Article in Chinese | WPRIM | ID: wpr-860957
ABSTRACT

Objective:

To explore the changes of the graph theory metrics and the functional connectivity (FC) of adolescent depression with attempted suicide.

Methods:

Resting-state functional MRI (rs-fMRI) data of 33 patients of adolescent depression with attempted suicide (attempted suicide group), 40 patients of adolescent depression without attempted suicide (no suicide group) and 52 normal adolescents (NC group) were collected, and a binary network was constructed. The brain regions changed in the graph theory attribute were selected as the seeds, and FC between seed and all the other voxels within the whole brain was computed and compared among groups.

Results:

There were small world attributes in all subjects. Compared with NC group, the degree of centrality significantly decreased in adolescent depression patients (P<0.01), and there were negative correlations between the decreased degree of centrality and Hamilton Depression Scale scores (r=-0.31, P<0.01). Compared with NC group, FC of the somatomotor networks and the salience network decreased in adolescent depression patients, while FC of the attention network in attempted suicide group increased than in no suicide group (all TFCE correction, P<0.05).

Conclusion:

Decrease of visual network information transmission ability in patients of adolescent depression is related to the severity of depression. The abnormal attention space ability caused by the attention network lesions may be the pathological mechanism of suicidal attempt in patients of adolescent depression.

Full text: Available Index: WPRIM (Western Pacific) Language: Chinese Journal: Chinese Journal of Medical Imaging Technology Year: 2020 Type: Article

Similar

MEDLINE

...
LILACS

LIS

Full text: Available Index: WPRIM (Western Pacific) Language: Chinese Journal: Chinese Journal of Medical Imaging Technology Year: 2020 Type: Article