Alteration of single‐subject gray matter networks in major depressed patients with suicidality

• Published in: Journal of magnetic resonance imaging Vol. 54; no. 1; pp. 215 - 224
• Main Authors: Li, Huiru, Yang, Jing, Yin, Li, Zhang, Huawei, Zhang, Feifei, Chen, Ziqi, Jia, Zhiyun, Gong, Qiyong
• Format: Journal Article
• Language: English
• Published: Hoboken, USA John Wiley & Sons, Inc 01.07.2021; Wiley Subscription Services, Inc
• Subjects: Cerebrospinal fluid; Circuits; Clustering; Computational neuroscience; Correlation analysis; Efficiency; Graph theory; gray matter; Image acquisition; Magnetic resonance; Magnetic resonance imaging; major depressive disorder; Medical imaging; Mental health; Neostriatum; Networks; Neural networks; pyschoradiology; Regression analysis; Statistical analysis; Statistical tests; structural networks; Substantia alba; Substantia grisea; suicidality; Suicides & suicide attempts; Thalamus; Therapeutic targets; Topology; Variance analysis; pyschoradiology; suicidality; major depressive disorder; structural networks; gray matter
• ISSN: 1053-1807; 1522-2586; 1522-2586
• DOI: 10.1002/jmri.27499

While regional brain alterations and functional connectivity in depressed suicidal patients have previously been reported, knowledge about gray matter (GM) structural networks is limited. The aim of this study was to explore the GM of depressed suicidal brains from the single‐subject structural network level. This was a cross‐sectional study, in which 50 healthy controls (HC, 31 ± 9 years), 50 major depressed patients without suicidality (NSD, 29 ± 10 years), and 50 major depressed patients with suicidality (SU, 29 ± 12 years) were enrolled. T1‐weighted images (T1WI) were acquired with three‐dimensional‐magnetization prepared rapid gradient echo sequence in 3.0 T magnetic resonance. The analysis was performed using the automated Computational Anatomy Toolbox (CAT12) within Statistical Parametric Mapping while running MATLAB. The T1 images were segmented into GM, white matter, and cerebrospinal fluid. Then single‐subject structural networks were constructed based on the morphological similarity of GM regions. Global network topological properties, including clustering coefficient (Cp), characterpath length (Lp), normalized clustering coefficient (γ), normalized characteristic path length (λ), small‐worldness (σ), global efficiency (Eglob), local efficiency (Eloc), and nodal network topological properties, including nodal efficiency, degree, and betweenness centrality, were measured using graph theory analysis. Statistical tests performed were analysis of variance, Pearson correlation analysis, and multiple linear regression analysis. Decreased Eglob and increased shortest Lp were observed in SU group compared to HC and NSD groups (p < 0.05). The NSD and SU groups had an increased λ and decreased Eloc compared to the HC group (p < 0.05). Altered nodal efficiency was found in the fronto‐striatum‐limbic‐thalamic circuit in the SU group compared with the HC and NSD groups (all p < 0.05). The GM network in the SU group showed decreased segregation and weaker integration, that is weaker small‐worldness, compared to the NSD and HC groups. Abnormal nodal efficiency was found in the fronto‐striatum‐limbic‐thalamic circuit in suicidal brains. This study provides new evidence for therapeutic targets for patients with depression and suicidality. Level of Evidence 3 Technical Efficacy Stage 3