Institute of Systems Neuroscience, Medical Faculty, Heinrich Heine University Düsseldorf, Düsseldorf, Germany.
Institute of Neuroscience and Medicine, Brain & Behavior (INM-7), Research Centre Jülich, Jülich, Germany.
Hum Brain Mapp. 2017 Dec;38(12):5845-5858. doi: 10.1002/hbm.23763. Epub 2017 Sep 6.
Previous whole-brain functional connectivity studies achieved successful classifications of patients and healthy controls but only offered limited specificity as to affected brain systems. Here, we examined whether the connectivity patterns of functional systems affected in schizophrenia (SCZ), Parkinson's disease (PD), or normal aging equally translate into high classification accuracies for these conditions. We compared classification performance between pre-defined networks for each group and, for any given network, between groups. Separate support vector machine classifications of 86 SCZ patients, 80 PD patients, and 95 older adults relative to their matched healthy/young controls, respectively, were performed on functional connectivity in 12 task-based, meta-analytically defined networks using 25 replications of a nested 10-fold cross-validation scheme. Classification performance of the various networks clearly differed between conditions, as those networks that best classified one disease were usually non-informative for the other. For SCZ, but not PD, emotion-processing, empathy, and cognitive action control networks distinguished patients most accurately from controls. For PD, but not SCZ, networks subserving autobiographical or semantic memory, motor execution, and theory-of-mind cognition yielded the best classifications. In contrast, young-old classification was excellent based on all networks and outperformed both clinical classifications. Our pattern-classification approach captured associations between clinical and developmental conditions and functional network integrity with a higher level of specificity than did previous whole-brain analyses. Taken together, our results support resting-state connectivity as a marker of functional dysregulation in specific networks known to be affected by SCZ and PD, while suggesting that aging affects network integrity in a more global way. Hum Brain Mapp 38:5845-5858, 2017. © 2017 Wiley Periodicals, Inc.
先前的全脑功能连接研究已经成功地对患者和健康对照组进行了分类,但对于受影响的大脑系统,仅提供了有限的特异性。在这里,我们研究了精神分裂症(SCZ)、帕金森病(PD)或正常衰老患者受影响的功能系统的连接模式是否同样可以为这些疾病提供高分类准确性。我们比较了每个组的预定义网络之间的分类性能,以及对于任何给定的网络,在组之间的分类性能。分别对 86 名 SCZ 患者、80 名 PD 患者和 95 名老年人相对于其匹配的健康/年轻对照组的功能连接进行了 25 次嵌套 10 倍交叉验证方案的 12 个基于任务的、元分析定义的网络的支持向量机分类。使用各种网络的分类性能在不同条件之间明显不同,因为那些最能分类一种疾病的网络通常对其他疾病没有信息。对于 SCZ,但不是 PD,情绪处理、同理心和认知动作控制网络最能准确地区分患者与对照组。对于 PD,但不是 SCZ,自传体或语义记忆、运动执行和心理理论认知网络的网络产生了最佳分类。相比之下,年轻老年人的分类基于所有网络,并且优于两种临床分类。我们的模式分类方法以比以前的全脑分析更高的特异性捕获了临床和发展条件之间的关联以及功能网络的完整性。总的来说,我们的结果支持静息状态连接作为特定网络中功能失调的标记,这些网络已知受 SCZ 和 PD 的影响,而表明衰老以更全局的方式影响网络完整性。人类大脑映射 38:5845-5858,2017。©2017 威利期刊公司