Resting‐state cerebral blood flow and functional connectivity abnormalities in depressed patients with childhood maltreatment: Potential biomarkers of vulnerability?

重性抑郁障碍 脑血流 眶额皮质 心理学 静息状态功能磁共振成像 神经心理学 前额叶皮质 神经影像学 神经科学 颞叶 额叶 内科学 医学 认知 癫痫
作者
Sangni Liu,Dandan Fan,Cancan He,Xinyi Liu,Haisan Zhang,Hongxing Zhang,Zhijun Zhang,Chunming Xie
出处
期刊:Psychiatry and Clinical Neurosciences [Wiley]
卷期号:78 (1): 41-50
标识
DOI:10.1111/pcn.13603
摘要

Aim Childhood maltreatment (CM) is an important risk factor for major depressive disorder (MDD). This study aimed to explore the specific effect of CM on cerebral blood flow (CBF) and brain functional connectivity (FC) in MDD patients. Methods A total of 150 subjects were collected including 55 MDD patients with CM, 34 MDD patients without CM, 19 healthy controls (HC) with CM, and 42 HC without CM. All subjects completed MRI scans and neuropsychological tests. Two‐way analysis of covariance was used to detect the main and interactive effects of disease and CM on CBF and FC across subjects. Then, partial correlation analyses were conducted to explore the behavioral significance of altered CBF and FC in MDD patients. Finally, a support vector classifier model was applied to differentiate MDD patients. Results MDD patients represented increased CBF in bilateral temporal lobe and decreased CBF in right visual cortex. Importantly, significant depression‐by‐CM interactive effects on CBF were primarily located in the frontoparietal regions, including orbitofrontal cortex (OFC), lateral prefrontal cortex (PFC), and parietal cortex. Moreover, significant FC abnormalities were seen in OFC‐PFC and frontoparietal‐visual cortex. Notably, the abnormal CBF and FC were significantly associated with behavioral performance. Finally, a combination of altered CBF and FC behaved with a satisfactory classification ability to differentiate MDD patients. Conclusions These results highlight the importance of frontoparietal and visual cortices for MDD with CM experience, proposing a potential neuroimaging biomarker for MDD identification.
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