Annu Int Conf IEEE Eng Med Biol Soc. 2022 Jul;2022:247-250. doi: 10.1109/EMBC48229.2022.9871542.
Neuropsychiatric disorders affect millions of people worldwide every year. Recent studies showed that the symptomatic overlaps across neuropsychiatric disorders mislead schizophrenia and bipolar disorder diagnosis. Additionally, recent studies claimed that schizoaffective disorder as a condition overlapped with both schizophrenia and bipolar disorder. Since symptomatic overlap among these disorders causes misdiagnosis, a need for neuroimaging biomarkers differentiating these disorders for a more accurate diagnosis is crucial. This study investigates dynamics functional network connectivity (dFNC) in the default mode network (DMN) of schizophrenia, bipolar, and schizoaffective disorder patients and compares them with their relative and healthy control. Additionally, it explored whether DMN dFNC features can predict the symptom severity of these neuropsychiatric disorders. Here, we found that dFNC features can differentiate schizophrenia from bipolar disorder. At the same time, we did not see a significant difference between schizoaffective with other conditions. Additionally, we found dFNC features can predict symptom severity of these three conditions.
神经精神疾病每年影响着全球数百万人。最近的研究表明，神经精神疾病之间的症状重叠导致了精神分裂症和双相情感障碍诊断的混淆。此外，最近的研究声称，分裂情感障碍是一种与精神分裂症和双相情感障碍都有重叠的疾病。由于这些疾病之间的症状重叠导致了误诊，因此需要神经影像学生物标志物来区分这些疾病，以便更准确地诊断。本研究调查了精神分裂症、双相情感障碍和分裂情感障碍患者的默认模式网络（DMN）中的动态功能网络连接（dFNC），并将其与他们的亲属和健康对照组进行了比较。此外，还探讨了 DMN dFNC 特征是否可以预测这些神经精神疾病的症状严重程度。在这里，我们发现 dFNC 特征可以区分精神分裂症和双相情感障碍。同时，我们没有看到分裂情感障碍与其他疾病之间有显著差异。此外，我们发现 dFNC 特征可以预测这三种疾病的症状严重程度。
