A significant risk factor for poststroke depression: the depression-related subnetwork

A significant risk factor for poststroke depression: the depression-related subnetwork

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J Psychiatry Neurosci 2015;40(3):259-268

Songran Yang, PhD*; Ping Hua, PhD*; Xinyuan Shang, MM; Zaixu Cui, MM; Suyu Zhong, MM; Gaolang Gong, PhD; Glyn W. Humphreys, PhD

Abstract

Background: Despite being one of the direct causes of depression, whether stroke-induced neuroanatomical deterioration actually plays an important role in the onset of poststroke depression (PSD) is controversial. We assessed the structural basis of PSD, particularly with regard to white matter connectivity.

Methods: We evaluated lesion index, fractional anisotropy (FA) reduction and brain structural networks and then analyzed whole brain voxel-based lesions and FA maps. To understand brain damage in the context of brain connectivity, we used a graph theoretical approach. We selected nodes whose degree correlated with the Hamilton Rating Scale for Depression score (p < 0.05, false discovery rate–corrected), after controlling for age, sex, years of education, lesion size, Mini Mental State Examination score and National Institutes of Health Stroke Scale score. We used Poisson regression with robust standard errors to assess the contribution of the identified network toward poststroke major depression. Results: We included 116 stroke patients in the study. Fourteen patients (12.1%) had diagnoses of major depression and 26 (22.4%) had mild depression. We found that lesions in the right insular cortex, left putamen and right superior longitudinal fasciculus as well as FA reductions in broader areas were all associated with major depression. Seventeen nodes were selected to build the depression-related subnetwork. Decreased local efficiency of the subnetwork was a significant risk factor for poststroke major depression (relative risk 0.84, 95% confidence interval 0.72-0.98, p = 0.027).

Limitations: The inability of DTI tractography to process fibre crossings may have resulted in inaccurate construction of white matter networks and affected statistical findings.

Conclusion: The present study provides, to our knowledge, the first graph theoretical analysis of white matter networks linked to poststroke major depression. These findings provide new insights into the neuroanatomical substrates of depression that develops after stroke.


*These authors contributed equally to this work.

Submitted Mar. 21, 2014, 2014; Revised Sept. 10, Oct. 17, Nov. 9, 2014; Accepted Nov. 10, 2014; Early-released Apr. 14, 2015.

Affiliations: From the Department of Experimental Psychology, University of Oxford, Oxford, United Kingdom (Yang, Humphreys); the Department of Cardiovascular Surgery, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, China (Hua); the Department of Neurology, Guangzhou First People’s Hospital, Guangzhou Medical University, Guangzhou, China (Shang); and the State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China (Cui, Zhong, Gong).

Funding: This work was supported by the National Natural Science Foundation of China (# 81000508) and the Pearl River Science and Technology Star Fund (# 2012J2200090).

Competing interests: None declared.

Contributors: S. Yang, P. Hua and G. Gong designed the study. S. Yang, P. Hua, X. Shang, Z. Cui and S. Zhong acquired and analyzed the data, which G. Humphreys also analyzed. S. Yang, P. Hua, X. Shang, Z. Cui and S. Zhong wrote the article, which all authors reviewed and approved for publication.

DOI: 10.1503/jpn.140086

Correspondence to: S. Yang, Department of Experimental Psychology, University of Oxford, 9 South Parks Road, Oxford OX1 3UD, United Kingdom; solovita.yang@gmail.com