Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China.
The School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China.
Hum Brain Mapp. 2022 Aug 1;43(11):3486-3497. doi: 10.1002/hbm.25862. Epub 2022 Apr 7.
Incidence of schizophrenia (SZ) has two predominant peaks, in adolescent and young adult. Early-onset schizophrenia provides an opportunity to explore the neuropathology of SZ early in the disorder and without the confound of antipsychotic mediation. However, it remains unexplored what deficits are shared or differ between adolescent early-onset (EOS) and adult-onset schizophrenia (AOS) patients. Here, based on 529 participants recruited from three independent cohorts, we explored AOS and EOS common and unique co-varying patterns by jointly analyzing three MRI features: fractional amplitude of low-frequency fluctuations (fALFF), gray matter (GM), and functional network connectivity (FNC). Furthermore, a prediction model was built to evaluate whether the common deficits in drug-naive SZ could be replicated in chronic patients. Results demonstrated that (1) both EOS and AOS patients showed decreased fALFF and GM in default mode network, increased fALFF and GM in the sub-cortical network, and aberrant FNC primarily related to middle temporal gyrus; (2) the commonly identified regions in drug-naive SZ correlate with PANSS positive significantly, which can also predict PANSS positive in chronic SZ with longer duration of illness. Collectively, results suggest that multimodal imaging signatures shared by two types of drug-naive SZ are also associated with positive symptom severity in chronic SZ and may be vital for understanding the progressive schizophrenic brain structural and functional deficits.
精神分裂症(SZ)的发病率有两个主要高峰,分别在青少年和年轻成人期。早发性精神分裂症为探索精神分裂症的神经病理学提供了机会,可以在疾病早期且没有抗精神病药物影响的情况下进行探索。然而,青少年早发性(EOS)和成人起病精神分裂症(AOS)患者之间存在哪些共同或不同的缺陷仍然未知。在这里,我们基于从三个独立队列中招募的 529 名参与者,通过联合分析三种 MRI 特征:低频振幅(fALFF)、灰质(GM)和功能网络连通性(FNC),来探索 AOS 和 EOS 的共同和独特的协变量模式。此外,还构建了一个预测模型,以评估在未经药物治疗的 SZ 患者中是否可以复制药物治疗的 SZ 患者的共同缺陷。结果表明:(1)EOS 和 AOS 患者的默认模式网络的 fALFF 和 GM 降低,皮质下网络的 fALFF 和 GM 增加,并且异常的 FNC 主要与颞中回有关;(2)在未经药物治疗的 SZ 中共同识别的区域与 PANSS 阳性显著相关,并且也可以预测慢性 SZ 中 PANSS 阳性与疾病持续时间较长相关。总之,结果表明,两种未经药物治疗的 SZ 共享的多模态成像特征也与慢性 SZ 的阳性症状严重程度相关,并且可能对理解进行性精神分裂症大脑结构和功能缺陷至关重要。