Melbourne Neuropsychiatry Centre, Department of Psychiatry, The University of Melbourne and Melbourne Health, Carlton South, Victoria, Australia.
Melbourne School of Engineering, The University of Melbourne, Parkville, Victoria, Australia.
Schizophr Bull. 2021 Jul 8;47(4):1156-1167. doi: 10.1093/schbul/sbab015.
Recent network-based analyses suggest that schizophrenia symptoms are intricately connected and interdependent, such that central symptoms can activate adjacent symptoms and increase global symptom burden. Here, we sought to identify key clinical and neurobiological factors that relate to symptom organization in established schizophrenia.
A symptom comorbidity network was mapped for a broad constellation of symptoms measured in 642 individuals with a schizophrenia-spectrum disorder. Centrality analyses were used to identify hub symptoms. The extent to which each patient's symptoms formed clusters in the comorbidity network was quantified with cluster analysis and used to predict (1) clinical features, including illness duration and psychosis (positive symptom) severity and (2) brain white matter microstructure, indexed by the fractional anisotropy (FA), in a subset (n = 296) of individuals with diffusion-weighted imaging (DWI) data.
Global functioning, substance use, and blunted affect were the most central symptoms within the symptom comorbidity network. Symptom profiles for some patients formed highly interconnected clusters, whereas other patients displayed unrelated and disconnected symptoms. Stronger clustering among an individual's symptoms was significantly associated with shorter illness duration (t = 2.7; P = .0074), greater psychosis severity (ie, positive symptoms expression) (t = -5.5; P < 0.0001) and lower fractional anisotropy in fibers traversing the cortico-cerebellar-thalamic-cortical circuit (r = .59, P < 0.05).
Symptom network structure varies over the course of schizophrenia: symptom interactions weaken with increasing illness duration and strengthen during periods of high positive symptom expression. Reduced white matter coherence relates to stronger symptom clustering, and thus, may underlie symptom cascades and global symptomatic burden in individuals with schizophrenia.
最近基于网络的分析表明，精神分裂症症状错综复杂且相互依存，以致核心症状可激活相邻症状并增加整体症状负担。在此，我们试图确定与既定精神分裂症中症状组织相关的关键临床和神经生物学因素。
我们为 642 名精神分裂症谱系障碍患者所测的广泛症状描绘了一种症状共病网络。采用中心度分析来识别核心症状。通过聚类分析来量化每个患者症状在共病网络中形成簇的程度，并用于预测（1）临床特征，包括疾病持续时间和精神病（阳性症状）严重程度，以及（2）在具有弥散加权成像（DWI）数据的亚组（n = 296）个体中的脑白质微观结构，用各向异性分数（FA）表示。
总体功能、物质使用和快感缺失是症状共病网络中最核心的症状。一些患者的症状谱形成了高度互联的簇，而其他患者的症状则不相关且不相连。个体症状聚类程度较强与疾病持续时间较短（t = 2.7；P =.0074）、精神病严重程度较高（即阳性症状表达）（t = -5.5；P < 0.0001）以及穿过皮质-小脑-丘脑-皮质回路的纤维的各向异性分数较低（r =.59，P < 0.05）显著相关。
精神分裂症过程中症状网络结构存在差异：随着疾病持续时间的增加，症状相互作用减弱，而在高阳性症状表达期间则增强。白质连贯性降低与症状聚类程度较强相关，因此，可能是精神分裂症个体中症状级联和整体症状负担的基础。
