Chen Meiling, Shao Heng, Wang Libo, Ma Jianing, Chen Jin, Li Junying, Zhong Jingmei, Zhu Baosheng, Bi Bin, Chen Kexuan, Wang Jiaojian, Gong Liang
Faculty of Life Science and Technology, Kunming University of Science and Technology, Kunming, China; Department of Clinical Psychology, the First People's Hospital of Yunnan Province, The Affiliated Hospital of Kunming University of Science and Technology, Kunming, China.
Department of Geriatrics, the First People's Hospital of Yunnan Province, The Affiliated Hospital of Kunming University of Science and Technology, Kunming, China.
Prog Neuropsychopharmacol Biol Psychiatry. 2025 Jan 10;136:111158. doi: 10.1016/j.pnpbp.2024.111158. Epub 2024 Oct 3.
Insomnia is increasingly prevalent with significant associations with depression. Delineating specific neural circuits for chronic insomnia disorder (CID) with and without depressive symptoms is fundamental to develop precision diagnosis and treatment. In this study, we examine static, dynamic and network topology changes of individual large-scale functional network for CID with (CID-D) and without depression to reveal their specific neural underpinnings. Seventeen individual-specific functional brain networks are obtained using a regularized nonnegative matrix factorization technique. Disorders-shared and -specific differences in static and dynamic large-scale functional network connectivities within or between the cognitive control network, dorsal attention network, visual network, limbic network, and default mode network are found for CID and CID-D. Additionally, CID and CID-D groups showed compromised network topological architecture including reduced small-world properties, clustering coefficients and modularity indicating decreased network efficiency and impaired functional segregation. Moreover, the altered neuroimaging indices show significant associations with clinical manifestations and could serve as effective neuromarkers to distinguish among healthy controls, CID and CID-D. Taken together, these findings provide novel insights into the neural basis of CID and CID-D, which may facilitate developing new diagnostic and therapeutic approaches.
失眠症日益普遍，且与抑郁症存在显著关联。明确伴有和不伴有抑郁症状的慢性失眠症（CID）的特定神经回路，对于精准诊断和治疗至关重要。在本研究中，我们研究了伴有（CID-D）和不伴有抑郁症的CID个体大规模功能网络的静态、动态和网络拓扑变化，以揭示其特定的神经基础。使用正则化非负矩阵分解技术获得了17个个体特异性功能性脑网络。发现CID和CID-D在认知控制网络、背侧注意网络、视觉网络、边缘网络和默认模式网络内部或之间的静态和动态大规模功能网络连通性方面存在疾病共享和特异性差异。此外，CID和CID-D组显示网络拓扑结构受损，包括小世界特性、聚类系数和模块化降低，表明网络效率降低和功能分离受损。此外，改变的神经影像学指标与临床表现显著相关，可作为区分健康对照、CID和CID-D的有效神经标志物。综上所述，这些发现为CID和CID-D的神经基础提供了新的见解，可能有助于开发新的诊断和治疗方法。
