Zheng Sisi, Song Mingkang, Song Nan, Zhu Hong, Li Xue, Yin Dongqing, Liu Shanshan, Zhao Yan, Fang Meng, Ning Yanzhe, Jia Hongxiao
Beijing Key Laboratory of Mental Disorders, National Clinical Research Center for Mental Disorders & National Center for Mental Disorders, Beijing Anding Hospital, Capital Medical University, Beijing, 100088, China.
Advanced Innovation Center for Human Brain Protection, Capital Medical University, Beijing, 100069, China.
BMC Psychiatry. 2025 Jan 21;25(1):59. doi: 10.1186/s12888-025-06497-w.
Depersonalization-Derealization Disorder (DPRD) presents challenges in understanding its neurobiological underpinnings. Several neuroimaging studies have revealed altered brain function and structure in DPRD. However, the knowledge about large-scale dysfunctional brain networks in DPRD remains unknown.
A total of 47 drug-naïve DPRD patients and 49 healthy controls (HCs) were recruited and underwent resting-state functional scanning. After constructing large-scale brain networks, we calculated within-and between-network functional connectivity (FC) using the Schaefer and Tian atlas. The Support Vector Machine (SVM) model was employed to classify DPRD patients and provide features for DPRD patients concerning the dysfunctional large-scale brain networks. Finally, the correlation analysis was performed between altered functional connectivity of large-scale brain networks and scores of clinical assessments in DPRD patients.
Compared to HCs, we found significantly decreased FCs, within-networks across four brain networks and between-networks involving 18 pairs of brain networks in DPRD patients. Moreover, our results revealed a satisfactory classification accuracy (80%) of these decreased FCs for correctly identifying DPRD patients. Notably, a significant negative correlation was observed between the 'Self' factor of the CDS and the FC within the somatosensory-motor network.
Overall, disrupted FC of large-scale brain networks may contribute to understanding neurobiological underpinnings in DPRD. Our findings may provide potential targets for therapeutic interventions.
人格解体-现实解体障碍（DPRD）在理解其神经生物学基础方面存在挑战。多项神经影像学研究揭示了DPRD患者大脑功能和结构的改变。然而，关于DPRD患者大规模功能失调脑网络的知识仍然未知。
共招募了47例未服用过药物的DPRD患者和49名健康对照者（HCs），并进行静息态功能扫描。构建大规模脑网络后，我们使用Schaefer和Tian图谱计算网络内和网络间的功能连接（FC）。采用支持向量机（SVM）模型对DPRD患者进行分类，并提供有关功能失调的大规模脑网络的DPRD患者特征。最后，对DPRD患者大规模脑网络功能连接改变与临床评估得分进行相关性分析。
与HCs相比，我们发现DPRD患者在四个脑网络内的网络内FC以及涉及18对脑网络的网络间FC显著降低。此外，我们的结果显示，这些降低的FC对正确识别DPRD患者具有令人满意的分类准确率（80%）。值得注意的是，临床分离量表（CDS）的“自我”因子与体感运动网络内的FC之间存在显著负相关。
总体而言，大规模脑网络的FC破坏可能有助于理解DPRD的神经生物学基础。我们的发现可能为治疗干预提供潜在靶点。
