ISSN 0439-755X
CN 11-1911/B
主办:中国心理学会
   中国科学院心理研究所
出版:科学出版社

心理学报 ›› 2023, Vol. 55 ›› Issue (5): 740-751.doi: 10.3724/SP.J.1041.2023.00740

• 研究报告 • 上一篇    下一篇

抑郁症的人格类型及其脑功能连接基础

李彧1,2,3, 位东涛1,2, 邱江1,2()   

  1. 1西南大学认知与人格教育部重点实验室, 重庆 400715
    2西南大学心理学部, 重庆 400715
    3西南大学教育学部, 重庆 400715
  • 收稿日期:2022-01-28 出版日期:2023-05-25 发布日期:2023-02-14
  • 通讯作者: 邱江, E-mail: qiuj318@swu.edu.cn
  • 基金资助:
    重庆市自然科学基金(cstc2015jcyjA10106);中国博士后科学基金面上资助(2021M702705);重庆市博士后创新人才支持计划(A33600125)

Personality subtypes of depressive disorders and their functional connectivity basis

LI Yu1,2,3, WEI Dongtao1,2, QIU Jiang1,2()   

  1. 1Key Laboratory of Cognition and Personality, Ministry of Education, Southwest University, Chongqing 400715, China
    2Department of Psychology, Southwest University, Chongqing 400715, China
    3Faculty of Education, Southwest University, Chongqing 400715, China
  • Received:2022-01-28 Online:2023-05-25 Published:2023-02-14

摘要:

本研究采用功能随机森林的方法, 将聚类过程与抑郁症诊断相结合, 分别在抑郁症和控制组中识别了人格类型(神经质和外向性的组合), 并进一步探究了不同人格类型的静息态功能连接差异。聚类分析结果显示, 抑郁症以高神经质和低外向性趋势的个体为主, 但同样有低神经质和高外向性趋势的个体。控制组样本则以低神经质和高外向性个体为主。静息态功能连接的结果显示:在不考虑人格亚型的情况下, 抑郁症和控制组在杏仁核/海马/脑岛−边缘网络/默认网络/控制网络的功能连接上均无显著差异。在纳入聚类分析所划分的亚型进行统计后, 多种人格类型在左侧杏仁核/脑岛−边缘网络(以眶额皮质区域为主)的功能连接强度上呈现出显著差异。本研究基于个人视角识别的抑郁症人格类型更符合现实情况与个体认知模式, 具有潜在的临床应用价值, 并且其功能连接的差异对理解抑郁症异质性提供了神经层面的参考。

关键词: 神经质, 外向性, 静息态功能连接, 抑郁症, 以个体为中心

Abstract:

Heterogeneity among mental health issues has always attracted considerable attention, thereby restricting research on mental health and cognitive neuroscience. Additionally, the person-centred approach to personality research, which emphasizes population heterogeneity, has received more attention. On the other hand, the heterogeneity among depressive patients has been a problem that cannot be ignored (most studies ignored the actual situation and directly assumed sample homogeneity). A large number of empirical studies have provided evidence that isolated personality traits are often associated with depression. Only a few studies have considered the probable effect from a taxonomy perspective. Moreover, the neural mechanisms of personality types in depression remain unclear. This study aimed to reveal different personality subtypes of depressive disorders and elucidate subtypes from the perspective of resting-state functional connectivity.

Personality and resting-state functional imaging data of 135 depressive patients and 133 controls were collected. First, combined with “depression diagnosis”, the personality types in depressive patients and controls were identified through functional random forest. Specifically, neuroticism and extraversion (input features) were fitted with the diagnosis of depression by a random forest model. The random seeds were set to 1234, and 500 decision trees were fitted. The performance of the model was evaluated by tenfold cross-validation. Subsequently, the random forest algorithm generated a proximity matrix that represented the similarity between paired participants. Then, based on the proximity matrix, community detection clustering analysis was conducted on depressive patients and controls, and personality types associated with depression diagnosis were obtained. Finally, we selected nodes of the subcortical network as regions of interest according to the power-264 template and calculated the functional connectivity map of the region of interest to the whole brain. Based on the functional connectivity map, the differences in resting-state functional connectivity between the main types were compared.

Personality and resting-state functional imaging data of 159 depressive patients and 156 controls were collected. First, combined with “depression diagnosis”, the personality types in depressive patients and controls were identified through functional random forest. Specifically, neuroticism and extraversion (input features) were fitted with the diagnosis of depression by a random forest model. The random seeds were set to 1234, and 500 decision trees were fitted. The performance of the model was evaluated by tenfold cross-validation. Subsequently, the random forest algorithm generated a proximity matrix that represented the similarity between paired participants. Then, based on the proximity matrix, community detection clustering analysis was conducted on depressive patients and controls, and personality types associated with depression diagnosis were obtained. Finally, we selected the amygdala, hippocampus, insula (AAL atlas) and limbic network, default network, and control network (Schaefer-Yeo template) as regions of interest and calculated the functional connectivity of the subcortical regions to the networks. ANOVA was used to compare resting-state functional connectivity between the personality types.

The results showed the following. (1) Depression was more common among individuals with high neuroticism and low extraversion tendencies, but there were also individuals with low neuroticism and high extraversion tendencies. The controls were more likely to be individuals with low neuroticism and high extraversion. (2) The results of resting-state functional connectivity showed no significant difference between depression and controls. (3) The functional connectivity strength of the left amygdala/insula-limbic network was significantly different across personality subtypes.

In summary, the personality subtypes of depression identified by person-centred perspectives are more in line with reality and individual cognitive patterns, and they have potential clinical adaptive value. The findings of this study enhance the understanding of heterogeneity among depressive disorders.

Key words: neuroticism, extraversion, resting-state functional connectivity, depressive disorders, person-centred

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