It is difficult to distinguish schizophrenia (SZ), schizoaffective disorder (SAD), and bipolar disorder with psychosis (BPP) as their clinical diagnoses rely on symptoms that overlap. In this paper, we investigate if there is biological evidence to support the symptom-based clinical categories by looking across the three disorders using dynamic connectivity measures, and provide meaningful characteristics on which brain functional connectivity measures are commonly or uniquely impaired. Large-sample functional magnetic resonance image (fMRI) datasets from 623 subjects including 238 healthy controls (HCs), 113 SZ patients, 132 SAD patients, and 140 BPP patients were analyzed. First, we computed whole-brain dynamic functional connectivity (DFC) using a sliding-window technique, and then extracted the individual connectivity states by applying our previously proposed decomposition-based DFC analysis method. Next, with the features from the dominant connectivity state, we assessed the clinical categories by performing both four-group (SZ, SAD, BPP and healthy control groups) and pair-wise classification using a support vector machine within cross-validation. Furthermore, we comprehensively summarized the shared and unique connectivity alterations among the disorders. In terms of the classification performance, our method achieved 69% in the four-group classification and >80% in the between-group classifications for the mean overall accuracy; and yielded 66% in the four-group classification and >80% in the between-group classifications for the mean balanced accuracy. Through summarizing the features that were automatically selected in the classifications, we found that among the three symptom-related disorders, their disorder-common impairments primarily included the decreased connectivity strength between thalamus and cerebellum and the increased strength between postcentral gyrus and thalamus. The disorder-unique changes included more various brain regions, mainly in the temporal and frontal gyrus. Our work demonstrates that dynamic functional connectivity provides biological evidence that both common and unique impairments exist in psychosis sub-groups.

很难区分精神分裂症 (SZ)、分裂情感性障碍 (SAD) 和双相情感障碍伴精神病 (BPP)，因为它们的临床诊断依赖于重叠的症状。在本文中，我们通过使用动态连接测量来观察三种疾病，调查是否有生物学证据支持基于症状的临床分类，并提供大脑功能连接测量普遍或独特受损的有意义的特征。分析了 623 名受试者的大样本功能磁共振图像 (fMRI) 数据集，其中包括 238 名健康对照 (HC)、113 名 SZ 患者、132 名 SAD 患者和 140 名 BPP 患者。首先，我们使用滑动窗口技术计算全脑动态功能连接（DFC），然后应用我们之前提出的基于分解的 DFC 分析方法提取各个连接状态。接下来，利用主要连接状态的特征，我们通过在交叉验证中使用支持向量机执行四组（SZ、SAD、BPP 和健康对照组）和成对分类来评估临床类别。此外，我们全面总结了这些疾病之间共有的和独特的连接性改变。在分类性能方面，我们的方法在四组分类中实现了 69%，在组间分类中实现了 >80% 的平均总体准确率；平均平衡准确率在四组分类中达到 66%，在组间分类中达到 >80%。通过总结分类中自动选择的特征，我们发现在三种症状相关疾病中，其常见疾病损害主要包括丘脑和小脑之间的连接强度降低以及中央后回和丘脑之间的连接强度增加。这种疾病特有的变化包括更多不同的大脑区域，主要是颞回和额回。我们的工作表明，动态功能连接提供了生物学证据，表明精神病亚组中存在常见和独特的损伤。