钩束
扣带回前部
萧条(经济学)
惊恐障碍
病理生理学
神经科学
心理学
白质
神经影像学
医学
磁共振成像
精神科
内科学
焦虑
部分各向异性
认知
放射科
宏观经济学
经济
标识
DOI:10.1016/j.pnpbp.2018.04.013
摘要
Depression and panic disorder (PD) share the common pathophysiology from the perspectives of neurotransmitters. The relatively high comorbidity between depression and PD contributes to the substantial obstacles to differentiate from depression and PD, especially for the brain pathophysiology. There are significant differences in the diagnostic criteria between depression and PD. However, the paradox of similar pathophysiology and different diagnostic criteria in these two disorders were still the issues needing to be addressed. Therefore the clarification of potential difference in the field of neuroscience and pathophysiology between depression and PD can help the clinicians and scientists to understand more comprehensively about significant differences between depression and PD. The researchers should be curious about the underlying difference of pathophysiology beneath the significant distinction of clinical symptoms. In this review article, I tried to find some evidences for the differences between depression and PD, especially for neural markers revealed by magnetic resonance imaging (MRI). The distinctions of structural and functional alterations in depression and PD are reviewed. From the structural perspectives, PD seems to have less severe gray matter alterations in frontal and temporal lobes than depression. The study of white matter microintegrity reveals more widespread alterations in fronto-limbic circuit of depression patients than PD patients, such as the uncinate fasciculus and anterior thalamic radiation. PD might have a more restrictive pattern of structural alterations when compared to depression. For the functional perspectives, the core site of depression pathophysiology is the anterior subnetwork of resting-state network, such as anterior cingulate cortex, which is not significantly altered in PD. A possibly emerging pattern of fronto-limbic distinction between depression and PD has been revealed by these explorative reports. The future trend for machine learning and pattern recognition might confirm the differentiation pattern between depression and PD based on the explorative results.
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