A growing body of research has suggested that people with schizophrenia (SZ) exhibit altered patterns of functional and anatomical brain connectivity. For example, many previous resting state functional connectivity (rsFC) studies have shown that, compared to healthy controls (HC), people with SZ demonstrate hyperconnectivity between subregions of the thalamus and sensory cortices, as well as hypoconnectivity between subregions of the thalamus and prefrontal cortex. In addition to thalamic findings, hypoconnectivity between cingulo-opercular brain regions thought to be involved in salience detection has also been commonly reported in people with SZ. However, previous studies have largely relied on seed-based analyses. Seed-based approaches require researchers to define a single a priori brain region, which is then used to create a rsFC map across the entire brain. While useful for testing specific hypotheses, these analyses are limited in that only a subset of connections across the brain are explored. In the current manuscript, we leverage novel network statistical techniques in order to detect latent functional connectivity networks with organized topology that successfully differentiate people with SZ from HCs. Importantly, these techniques do not require a priori seed selection and allow for whole brain investigation, representing a comprehensive, data-driven approach to determining differential connectivity between diagnostic groups. Across two samples, (Sample 1: 35 SZ, 44 HC; Sample 2: 65 SZ, 79 HC), we found evidence for differential rsFC within a network including temporal and thalamic regions. Connectivity in this network was greater for people with SZ compared to HCs. In the second sample, we also found evidence for hypoconnectivity within a cingulo-opercular network of brain regions in people with SZ compared to HCs. In summary, our results replicate and extend previous studies suggesting hyperconnectivity between the thalamus and sensory cortices and hypoconnectivity between cingulo-opercular regions in people with SZ using data-driven statistical and graph theoretical techniques.

越来越多的研究表明，精神分裂症（SZ）的人表现出功能性和解剖性大脑连接性改变的模式。例如，以前的许多休息状态功能连接（rsFC）研究表明，与健康对照组（HC）相比，SZ患者表现出丘脑子区域与感觉皮层之间的高度连通性，以及丘脑和前额叶区域之间的低连通性皮层。除了丘脑发现，在SZ人群中也普遍报道了被认为与显着性检测有关的扣带-胆脑区域之间的连接不足。但是，以前的研究在很大程度上依赖于基于种子的分析。基于种子的方法要求研究人员定义单个先验条件大脑区域，然后将其用于创建整个大脑的rsFC图。这些分析虽然可用于检验特定的假设，但局限性在于只能探索大脑中的一部分连接。在当前的手稿中，我们利用新颖的网络统计技术来检测具有组织拓扑结构的潜在功能连接网络，从而成功地将SZ人与HC区分。重要的是，这些技术不需要先验种子选择并允许进行全脑研究，代表了一种综合的，数据驱动的方法来确定诊断组之间的差异连通性。在两个样本（样本1：35 SZ，44 HC；样本2：65 SZ，79 HC）中，我们发现了在包括颞和丘脑区域的网络中存在差异rsFC的证据。与HC相比，对于SZ人群来说，该网络的连通性更高。在第二个样本中，我们还发现了与HCs相比，SZ患者的大脑区域的耳蜗-耳蜗网络内连通性较低的证据。总之，我们的结果重复并扩展了以前的研究，这些研究表明，使用数据驱动的统计和图论技术，SZ患者的丘脑和感觉皮层之间的过度连通性以及扣带-小脑区域之间的低连通性。