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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.

Type

Journal article

Journal

J Psychiatry Neurosci

Publication Date

07/2015

Volume

40

Pages

259 - 268

Keywords

Aged, Brain, Brain Ischemia, Depressive Disorder, Diffusion Tensor Imaging, Female, Humans, Image Processing, Computer-Assisted, Male, Neural Pathways, Psychiatric Status Rating Scales, Regression Analysis, Risk Factors, Severity of Illness Index, Stroke