Brain network informed subject community detection in early-onset schizophrenia.
Brain network informed subject community detection in early-onset schizophrenia.
复制标题
大脑网络知情受试者社区检测早发性精神分裂症
DOI：
10.1038/srep05549
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发表时间：
2014-07-03
影响因子：
4.6
通讯作者：
Bandettini PA
中科院分区：
文献类型：
--
作者：
Yang Z;Xu Y;Xu T;Hoy CW;Handwerker DA;Chen G;Northoff G;Zuo XN;Bandettini PA
早发性精神分裂症（EOS）为研究精神分裂症的病理生理机制和发展提供了独特的机会。使用 26 名未曾接受药物的首发 EOS 患者和 25 名年龄和性别匹配的对照受试者，我们检查了 EOS 背后的内在连接网络 (ICN) 缺陷。由于基于行为的精神疾病分类系统与潜在的脑功能障碍之间出现了不一致，我们采用完全数据驱动的方法来研究受试者是否可以根据其 ICN 的特征分为高度同质的社区。然后将所得的受试者群落和 ICN 的代表性特征与临床诊断和多变量症状模式相关联。 EOS 患者中统计上不存在默认模式 ICN。另一个额颞叶 ICN 进一步区分了以阴性症状为主的 EOS 患者。具有明显阳性症状的 EOS 患者的第二个网络的连接模式与典型的对照组高度相似。我们的事后功能连接模型证实，额颞叶回路的连接强度受到 EOS 中阳性和阴性综合征的相对严重程度的显着调节。这项研究提出了一种基于大脑网络的新型亚型发现方法，并提出了大脑网络与 EOS 症状模式之间的复杂联系。
Early-onset schizophrenia (EOS) offers a unique opportunity to study pathophysiological mechanisms and development of schizophrenia. Using 26 drug-naïve, first-episode EOS patients and 25 age- and gender-matched control subjects, we examined intrinsic connectivity network (ICN) deficits underlying EOS. Due to the emerging inconsistency between behavior-based psychiatric disease classification system and the underlying brain dysfunctions, we applied a fully data-driven approach to investigate whether the subjects can be grouped into highly homogeneous communities according to the characteristics of their ICNs. The resultant subject communities and the representative characteristics of ICNs were then associated with the clinical diagnosis and multivariate symptom patterns. A default mode ICN was statistically absent in EOS patients. Another frontotemporal ICN further distinguished EOS patients with predominantly negative symptoms. Connectivity patterns of this second network for the EOS patients with predominantly positive symptom were highly similar to typically developing controls. Our post-hoc functional connectivity modeling confirmed that connectivity strength in this frontotemporal circuit was significantly modulated by relative severity of positive and negative syndromes in EOS. This study presents a novel subtype discovery approach based on brain networks and proposes complex links between brain networks and symptom patterns in EOS.