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Problems with Centrality Measures in Psychopathology Symptom Networks: Why Network Psychometrics Cannot Escape Psychometric Theory.精神病理学症状网络中心度测量的问题:为什么网络心理计量学无法逃避心理计量理论。
Multivariate Behav Res. 2021 Mar-Apr;56(2):199-223. doi: 10.1080/00273171.2019.1640103. Epub 2019 Aug 12.
2
A Hierarchical Taxonomy of Psychopathology Can Transform Mental Health Research.精神病理学的层次分类学可以改变心理健康研究。
Perspect Psychol Sci. 2019 May;14(3):419-436. doi: 10.1177/1745691618810696. Epub 2019 Mar 7.
3
Networks of Depression and Anxiety Symptoms Across Development.抑郁和焦虑症状的发展网络。
J Am Acad Child Adolesc Psychiatry. 2018 Dec;57(12):964-973. doi: 10.1016/j.jaac.2018.05.027. Epub 2018 Sep 26.
4
Don't blame the model: Reconsidering the network approach to psychopathology.别责怪模型:重新思考精神病理学的网络方法。
Psychol Rev. 2018 Jul;125(4):606-615. doi: 10.1037/rev0000108.
5
The association between executive functioning and psychopathology: general or specific?执行功能与精神病理学之间的关系:一般的还是特定的?
Psychol Med. 2018 Aug;48(11):1787-1794. doi: 10.1017/S0033291717003269. Epub 2018 Mar 9.
6
Transdiagnostic Symptom Clusters and Associations With Brain, Behavior, and Daily Function in Mood, Anxiety, and Trauma Disorders.跨诊断症状群与心境、焦虑和创伤障碍中的大脑、行为和日常功能的关联。
JAMA Psychiatry. 2018 Feb 1;75(2):201-209. doi: 10.1001/jamapsychiatry.2017.3951.
7
Evidence that psychopathology symptom networks have limited replicability.精神病理学症状网络具有有限可重复性的证据。
J Abnorm Psychol. 2017 Oct;126(7):969-988. doi: 10.1037/abn0000276.
8
Measuring the hierarchical general factor model of psychopathology in young adults.测量青年人群精神病理学的层级广义因子模型。
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10
The Hierarchical Taxonomy of Psychopathology (HiTOP): A dimensional alternative to traditional nosologies.精神病理学的分层分类法(HiTOP):传统分类法的维度替代方案。
J Abnorm Psychol. 2017 May;126(4):454-477. doi: 10.1037/abn0000258. Epub 2017 Mar 23.

评估神经认知作为一种跨诊断构念的可行性,同时使用潜在变量模型和网络分析。

Evaluating the Viability of Neurocognition as a Transdiagnostic Construct Using Both Latent Variable Models and Network Analysis.

机构信息

Department of Psychological and Brain Sciences, G60 Psychological and Brain Sciences Bldg, University of Iowa, Iowa City, IA, US.

Division of Psychology, Department of Psychiatry, Oregon Health & Science University, Oregon, Portland, US.

出版信息

Res Child Adolesc Psychopathol. 2021 Jun;49(6):697-710. doi: 10.1007/s10802-021-00770-8. Epub 2021 Feb 3.

DOI:10.1007/s10802-021-00770-8
PMID:33534092
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC8713463/
Abstract

The relational structure of psychological symptoms and disorders is of crucial importance to mechanistic and causal research. Methodologically, factor analytic approaches (latent variable modeling) and network analyses are two dominant approaches. Amidst some debate about their relative merits, use of both methods simultaneously in the same data set has rarely been reported in child or adolescent psychopathology. A second issue is that the nosological structure can be enriched by inclusion of transdiagnostic constructs, such as neurocognition (e.g., executive functions and other processes). These cut across traditional diagnostic boundaries and are rarely included even though they can help map the mechanistic architecture of psychopathology. Using a sample enriched for ADHD (n = 498 youth ages 6 to 17 years; M = 10.8 years, SD = 2.3 years, 55% male), both approaches were used in two ways: (a) to model symptom structure and (b) to model seven neurocognitive domains hypothesized as important transdiagnostic features in ADHD and associated disorders. The structure of psychopathology domains was similar across statistical approaches with internalizing, externalizing, and neurocognitive performance clusters. Neurocognition remained a distinct domain according to both methods, showing small to moderate associations with internalizing and externalizing domains in latent variable models and high connectivity in network analyses. Overall, the latent variable and network approaches yielded more convergent than discriminant findings, suggesting that both may be complementary tools for evaluating the utility of transdiagnostic constructs for psychopathology research.

摘要

心理症状和障碍的关系结构对于机械和因果研究至关重要。在方法学上,因素分析方法(潜在变量建模)和网络分析是两种主要方法。尽管这两种方法各有优缺点,但在儿童或青少年精神病理学中,同时在同一数据集上使用这两种方法的情况很少见。第二个问题是,通过纳入跨诊断的结构,如神经认知(例如,执行功能和其他过程),可以丰富疾病分类结构。这些结构跨越了传统的诊断边界,即使它们可以帮助绘制精神病理学的机械结构,也很少被包括在内。使用一个富含 ADHD 的样本(n=498 名 6 至 17 岁的青少年;M=10.8 岁,SD=2.3 岁,55%为男性),这两种方法以两种方式使用:(a)建模症状结构,(b)建模七个神经认知领域,这些领域被假设为 ADHD 和相关障碍的重要跨诊断特征。精神病理学领域的结构在统计方法上是相似的,包括内化、外化和神经认知表现集群。根据这两种方法,神经认知仍然是一个独特的领域,在潜在变量模型中与内化和外化领域呈小到中度相关,在网络分析中具有高度的连接性。总体而言,潜在变量和网络方法得出的结果更具一致性而非区分性,这表明这两种方法都可能是评估跨诊断结构对精神病理学研究的效用的互补工具。