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躁狂发作前单相抑郁向双相障碍的早期识别:来自动态 fMRI 的证据。

Early identification of bipolar from unipolar depression before manic episode: Evidence from dynamic rfMRI.

机构信息

School of Biological Sciences & Medical Engineering, Southeast University, Nanjing, China.

Child Development and Learning Science, Key Laboratory of Ministry of Education, Nanjing, China.

出版信息

Bipolar Disord. 2019 Dec;21(8):774-784. doi: 10.1111/bdi.12819. Epub 2019 Sep 18.

Abstract

OBJECTIVE

Misdiagnosis of bipolar disorder (BD) as unipolar disorder (UD) may cause improper treatment strategy to be chosen, especially in the early stages of disease. The aim of this study was to characterize alterations in specific brain networks for depressed patients who transformed into BD (tBD) from UD.

METHOD

The module allegiance from resting-fMRI by applying a multilayer modular method was estimated in 99 patients (33 tBD, 33 BD, 33 UD) and 33 healthy controls (HC). A classification model was trained on tBD and UD patients. HC was used to explore the functional declination patterns of BD, tBD, and UD.

RESULTS

Based on our classification model, difference mainly reflected in default-mode network (DMN). Compared with HC, both BD and tBD focused on the difference of somatomotor network (SMN), while UD on the abnormity of DMN. The patterns of brain network between patients with BD and tBD were well-overlapped, except for cognitive control network (CCN).

CONCLUSION

The functional declination of internal interaction in DMN was suggested to be useful for the identification of BD from UD in the early stage. The higher recruitment of DMN may predispose patients to depressive states, while higher recruitment of SMN makes them more sensitive to external stimuli and prone to mania. Furthermore, CCN may be a critical network for identifying different stages of BD, suggesting that the onset of mania in depressed patients is accompanied by CCN related cognitive impairments.

摘要

目的

将双相障碍(BD)误诊为单相障碍(UD)可能导致选择不当的治疗策略,尤其是在疾病的早期阶段。本研究旨在描述从 UD 转为 BD(tBD)的抑郁患者特定大脑网络的变化。

方法

通过应用多层模块方法,对 99 名患者(33 名 tBD、33 名 BD、33 名 UD)和 33 名健康对照者(HC)的静息态 fMRI 进行模块隶属度估计。在 tBD 和 UD 患者上训练分类模型。使用 HC 探索 BD、tBD 和 UD 的功能下降模式。

结果

基于我们的分类模型,差异主要反映在默认模式网络(DMN)中。与 HC 相比,BD 和 tBD 均侧重于躯体运动网络(SMN)的差异,而 UD 则侧重于 DMN 的异常。BD 和 tBD 患者之间的脑网络模式重叠良好,除了认知控制网络(CCN)。

结论

DMN 内部相互作用的功能下降被认为有助于在早期识别 UD 中的 BD。DMN 的高募集可能使患者更容易处于抑郁状态,而 SMN 的高募集使他们对外界刺激更敏感,更容易出现躁狂。此外,CCN 可能是识别 BD 不同阶段的关键网络,表明抑郁患者的躁狂发作伴随着与 CCN 相关的认知障碍。

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