Background Schizophrenia, a multifaceted psychiatric disorder characterized by functional dysconnectivity, poses significant challenges in clinical practice. This study explores the potential of functional connectivity (FC)-based searchlight multivariate pattern analysis (CBS-MVPA) to discriminate between schizophrenia patients and healthy controls while also predicting clinical variables. Study Design We enrolled 112 schizophrenia patients and 119 demographically matched healthy controls. Resting-state functional magnetic resonance imaging data were collected, and whole-brain FC subnetworks were constructed. Additionally, clinical assessments and cognitive evaluations yielded a dataset comprising 36 clinical variables. Finally, CBS-MVPA was utilized to identify subnetworks capable of effectively distinguishing between the patient and control groups and predicting clinical scores. Study Results The CBS-MVPA approach identified 63 brain subnetworks exhibiting significantly high classification accuracies, ranging from 62.2% to 75.6%, in distinguishing individuals with schizophrenia from healthy controls. Among them, 5 specific subnetworks centered on the dorsolateral superior frontal gyrus, orbital part of inferior frontal gyrus, superior occipital gyrus, hippocampus, and parahippocampal gyrus showed predictive capabilities for clinical variables within the schizophrenia cohort. Conclusion This study highlights the potential of CBS-MVPA as a valuable tool for localizing the information related to schizophrenia in terms of brain network abnormalities and capturing the relationship between these abnormalities and clinical variables, and thus, deepens our understanding of the neurological mechanisms of schizophrenia.

中文翻译：

---

基于功能连接的探照灯多变量模式分析用于区分精神分裂症患者并预测临床变量


背景精神分裂症是一种以功能性连接障碍为特征的多方面精神疾病，给临床实践带来了重大挑战。本研究探讨了基于功能连接 (FC) 的探照灯多变量模式分析 (CBS-MVPA) 区分精神分裂症患者和健康对照同时预测临床变量的潜力。研究设计 我们招募了 112 名精神分裂症患者和 119 名人口统计匹配的健康对照者。收集静息态功能磁共振成像数据，构建全脑 FC 子网络。此外，临床评估和认知评估产生了包含 36 个临床变量的数据集。最后，利用 CBS-MVPA 来识别能够有效区分患者组和对照组并预测临床评分的子网络。研究结果 CBS-MVPA 方法识别出 63 个大脑子网络，在区分精神分裂症患者与健康对照者方面表现出极高的分类准确度，范围为 62.2% 至 75.6%。其中，以额上回背外侧、额下回眶部、枕上回、海马和海马旁回为中心的 5 个特定子网络显示出对精神分裂症队列中临床变量的预测能力。结论 这项研究强调了 CBS-MVPA 作为一种有价值的工具的潜力，可以在脑网络异常方面定位与精神分裂症相关的信息，并捕获这些异常与临床变量之间的关系，从而加深我们对精神分裂症神经机制的理解。